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22-02-2009, 12:35 AM
Post: #1
Photo Blue eyes (Download Full Report And Abstract)
What is Blue eyes?
The U.S. computer giant,IBM has been conducting research on the Blue Eyes technology at its Almaden Research Center (ARC) in San Jose, Calif., since 1997. The ARC is IBM's main laboratory for basic research. The primary objective of the research is to give a computer the ability of the human being to assess a situation by using the senses of sight, hearing and touch.
Animal survival depends on highly developed sensory abilities. Likewise, human cognition depends on highly developed abilities to perceive, integrate, and interpret visual, auditory, and touch information. Without a doubt, computers would be much more powerful if they had even a small fraction of the perceptual ability of animals or humans. Adding such perceptual abilities to computers would enable computers and humans to work together more as partners. Toward this end, the Blue Eyes project aims at creating computational devices with the sort of perceptual abilities that people take for granted.
Thus Blue eyes is the technology to make computers sense and understand human behavior and feelings and react in the proper ways.

Aims
1)To design smarter devices
2)To create devices with emotional intelligence
3)To create computational devices with perceptual abilities
The idea of giving computers personality or, more accurately, emotional intelligence" may seem creepy, but technologists say such machines would offer important advantages.
De-spite their lightning speed and awesome powers of computation, today's PCs are essentially deaf, dumb, and blind. They can't see you, they can't hear you, and they certainly don't care a whit how you feel. Every computer user knows the frustration of nonsensical error messages, buggy software, and abrupt system crashes. We might berate the computer as if it were an unruly child, but, of course, the machine can't respond. "It's ironic that people feel like dummies in front of their computers, when in fact the computer is the dummy," says Rosalind Picard, a computer science professor at the MIT Media Lab in Cambridge.

A computer endowed with emotional intelligence, on the other hand, could recognize when its operator is feeling angry or frustrated and try to respond in an appropriate fashion. Such a computer might slow down or replay a tutorial program for a confused student, or recognize when a designer is burned out and suggest he take a break. It could even play a recording of Beethoven's "Moonlight Sonata" if it sensed anxiety or serve up a rousing Springsteen anthem if it detected lethargy. The possible applications of "emotion technology" extend far beyond the desktop. A car equipped with an affective computing system could recognize when a driver is feeling drowsy and ad-vise her to pull over, or it might sense when a stressed-out motorist is about to explode and warn him to slow down and cool off.
These machines have got their own personality and this personality depends upon the moods of the user.

Human cognition depends primarily on the ability to perceive, interpret, and integrate audio-visuals and sensoring information. Adding extraordinary perceptual abilities to computers would enable computers to work together with human beings as intimate partners.Researchers are attempting to add more capabilities to computers that will allow them to interact like humans, recognize human presents, talk, listen, or even guess their feelings. The BLUE EYES technology aims at creating computational machines that have perceptual and sensory ability like those of human beings. It uses non-obtrusige sensing method, employing most modern video cameras and microphones to identifies the users actions through the use of imparted sensory abilities . The machine can understand what a user wants, where he is looking at, and even realize his physical or emotional states.


TRACKS USED
Our emotional changes are mostly reflected in our heart pulse rate,reathing rate ,facial expressions ,eye movements ,voice etc.Hence these are the parameters on which lue technology is being developed.

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31-01-2010, 07:57 PM
Post: #2
RE: Blue eyes (Download Full Report And Abstract)

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BLUE EYES

What is Blue eyes?
The U.S. computer giant,IBM has been conducting research on the Blue
Eyes technology at its Almaden Research Center (ARC) in San Jose,
Calif., since 1997. The ARC is IBM's main laboratory for basic
research. The primary objective of the research is to give a computer
the ability of the human being to assess a situation by using the
senses of sight, hearing and touch.
Animal survival depends on highly developed sensory abilities.
Likewise, human cognition depends on highly developed abilities to
perceive, integrate, and interpret visual, auditory, and touch
information. Without a doubt, computers would be much more powerful
if they had even a small fraction of the perceptual ability of
animals or humans. Adding such perceptual abilities to computers
would enable computers and humans to work together more as partners.
Toward this end, the Blue Eyes project aims at creating computational
devices with the sort of perceptual abilities that people take for
granted.
Thus Blue eyes is the technology to make computers sense and
understand human behavior and feelings and react in the proper ways.

Aims
1)To design smarter devices
2)To create devices with emotional intelligence
3)To create computational devices with perceptual abilities
The idea of giving computers personality or, more accurately,
emotional intelligence" may seem creepy, but technologists say such
machines would offer important advantages.
De-spite their lightning speed and awesome powers of computation,
today's PCs are essentially deaf, dumb, and blind. They can't see
you, they can't hear you, and they certainly don't care a whit how
you feel. Every computer user knows the frustration of nonsensical
error messages, buggy software, and abrupt system crashes. We might
berate the computer as if it were an unruly child, but, of course,
the machine can't respond. "It's ironic that people feel like dummies
in front of their computers, when in fact the computer is the dummy,"
says Rosalind Picard, a computer science professor at the MIT Media
Lab in Cambridge.
A computer endowed with emotional intelligence, on the other hand,
could recognize when its operator is feeling angry or frustrated and
try to respond in an appropriate fashion. Such a computer might slow
down or replay a tutorial program for a confused student, or
recognize when a designer is burned out and suggest he take a break.
It could even play a recording of Beethoven's "Moonlight Sonata" if
it sensed anxiety or serve up a rousing Springsteen anthem if it
detected lethargy. The possible applications of "emotion technology"
extend far beyond the desktop. A car equipped with an affective
computing system could recognize when a driver is feeling drowsy and
ad-vise her to pull over, or it might sense when a stressed-out
motorist is about to explode and warn him to slow down and cool off.
These machines have got their own personality and this
personality depends upon the moods of the user.

TRACKS USED
Our emotional changes are mostly reflected in our heart pulse
rate,reathing rate ,facial expressions ,eye movements ,voice
etc.Hence these are the parameters on which lue technology is being
developed.

Making computers see and feel
Blue Eyes uses sensing technology to identify a user's actions and to
extract key information. This information is then analyzed to
determine the user's physical, emotional, or informational state,
which in turn can be used to help make the user more productive by
performing expected actions or by providing expected information.
Beyond making computers more responsive to people's feelings,
researchers say there is another compelling reason for giving ma-
chines emotional intelligence. Contrary to the common wisdom that
emotions contribute to irrational behavior, studies have shown that
feelings actually play a vital role in logical thought and decision-
making. Emotionally impaired people often find it difficult to make
decisions because they fail to recognize the subtle clues and
signals--does this make me feel happy or sad, excited or bored?--that
help direct healthy thought processes. It stands to reason,
therefore, that computers that can emulate human emotions are more
likely to behave rationally, in a manner we can understand. Emotions
are like the weather.We only pay attention to them when there is a
sudden outburst, like a tornado, but in fact they are constantly
operating in the background, helping to monitor and guide our day-to
-day activities.
Picard, who is also the author of the groundbreaking book Affective
Computing, argues that computers should operate under the same
principle. "They have tremendous mathematical abilities, but when it
comes to interacting with people, they are autistic," she says. "If
we want computers to be genuinely intelligent and interact naturally
with us, we must give them the ability to recognize, understand, and
even to behave' and express emotions." Imagine the benefit of a
computer that could remember that a particular Internet search had
resulted in a frustrating and futile exploration of cyberspace. Next
time, it might modify its investigation to improve the chances of
success when a similar request is made.

Affective Computing
The process of making emotional computers with sensing abilities is
known as affective computing.The steps used in this are:
1)Giving sensing abilities
2)Detecting human emotions
3)Respond properly

The first step, researchers say, is to give ma-chines the equivalent
of the eyes, ears, and other sensory organs that humans use to
recognize and express emotion. To that end, computer scientists are
exploring a variety of mechanisms including voice-recognition
software that can discern not only what is being said but the tone in
which it is said; cameras that can track subtle facial expressions,
eye movements, and hand gestures; and biometric sensors that can
measure body temperature, blood pressure, muscle tension, and other
physiological signals associated with emotion.
In the second step, the computers have to detect even the
minor variations of our moods.For eg,a person may hit the keyboard
very fastly either in the happy mood or in the angry mood.
In the third step the computers have to react in accordance
with the emotional states.
Various methods of accomplishing affective computing are :
1) Affect detection:
This is the method of detecting our emotional states from
the expressions on our face.Algorithms amenale to real time
implementation that extract information from facial expressions and
head gestures are being explored.Most of the information is
extractewd from the position of the eye rows and the corners of the
mouth.
2)MAGIC pointing:
MAGIC stands for Manual Acquisition with Gaze Tracking
Technology. a computer with this technology could move the
cursor by following the direction of the user's eyes.
This type of technology will enable the computer to
automatically transmit information related to the screen
that the user is gazing at. Also, it will enable the
computer to determine, from the user's expression, if he or
she understood the information on the screen, before
automatically deciding to proceed to the next program.
The user pointing is still done by the hand,ut the cursor always
appears at the right position as if by MAGIC.By marrying input
technology and eye tracking ,we get MAGIC pointing.
3)SUITOR:
SUITOR stands for Simple User Interface Tracker.It
implements the method for putting computational devices in touch with
their userâ„¢s changing moods.It is mostly used in we ased
applications.By watching what we page the user is currently
rowsing,the SUITOR can find additional information on that topic.The
key is that the user simply interacts with the computer as usual and
the computer infers user interest based on what it sees the user do.

4)EMOTION MOUSE:
This is the mouse emedded with sensors that can sense the
physiological attributes such as temperature,ody prewssure,pulse
rate, and touching style, etc.The computer can determine the userâ„¢s
emotional states by a single touch.IBM is still oerforming research
on this mouse and will be availale in the market within the next two
or three years.The expected accuracy is 75%.

Blue Eyes enaled devices

Some of the blue Eyes enabled devices are discussed below:
1)POD:
The first blue Eye enabled mass production device
was POD ,the car manufactured y TOYOTA.It could keep the driver alert
and active.It could tell the driver to go slow if he is driving too
fastly and it could pull over the driver when he feels drowsy.Also it
could hear the driver some sort of interesting music when he is
getting bored.
2)PONG:
IBM released a robot designed for demonstrating the
new technology.The Blue Eyes robot is equipped with a computer
capable of analyzing a person's glances and other forms of
expressions of feelings, before automatically determining the next
type of action. IBM has released a robot called PONG, which is
equipped with the Blue Eyes technology. PONG is capable of
perceiving the person standing in front of it, smiles when the
person calls his name, and expresses loneliness when it loses sight
of the person.
IBM is showing this robot to the public at the company's
exhibition called "IBM Fair 2000" at the Japan Convention
Center (Makuhari Messe) in Chiba prefecture, March 1-3.
3)SECURE PAD:
This is an electronic badge that can identify the wearer
and track his movements and activities with an array of sensors. The
device was designed for a major health-care provider to track the
activities of doctors and other personnel at large medical
facilities. It knows who you are and where you are, and it has a
pretty good idea of what you are doing and when you are doing it.
Although that may sound ominous, the device will ultimately benefit
patients by enhancing the security and accountability of medical
facilities. For example, the device will know who has accessed a drug
locker and what drugs were removed. It will also allow doctors to
access confidential medical information without carrying around paper
charts, which can be misplaced or read by unauthorized personnel.
When a doctor wearing the Secure Pad enters a patient's room, the
patient's medical records will automatically appear on a wall monitor
when the doctor looks at it. When he looks away, or another person
enters the room, the records will disappear. Another advantage of
Secure Pad is that it's interchangeable; when a wearer removes the
badge from her body, the device automatically deactivates, its slate
wiped clean until the next person puts it on.

SOFTWARE and HARDWARE
BLue Eyes software is called agent which analyzes and modifies the
mother program, according to the userâ„¢s needs and moods.The user
modifies the mother program whenever the user requests or hardware
finds that the user is changing his moods.
For eg, in a we browsing ,if a particular search is found to e
failure ,the agent might improve the searching y changing the
searching key words so as to get the desired results.
Hardware used is dedicated hardware.Usually emedded devices are
used.For eg, a pc camera may drain the processing ailities of the
system and can not e used. The embedded devices have their own
processing capabilities and can function on their own to achieve
specific purposes.

CURRENT SCENARIO
Pioneers in this field are IBM,MIT(Massachussets Institute of
Technology),Sony.Egs of the technologies now under study have already
been discussed.Limited success is achieved in translating
neurological activities into identifiable emotional states
y implanting electrodes in the rain.Researches are still going on and
commercial availability is supposed to happen within the next few
years.


FUTURE
Future applications of this technology is limitless “from designing
cars and developing presentations to interactive entertainment and
advertising.Also it may become very common in our household devices
also .
For eg:
A blue eyes enabled TV set would become active when we look in its
directions.Voice commands could then tune your favourite channel and
adjust the volume.

CONCLUSION
The Blue eyes technology ensures a convenient way of simplifying the
life by providing more delicate and user friendly facilities in
computing devices. The gap between the electronic and
physical world will be reduced.The computers can be run by using the
implicit commands instead of the explicit commands.

REFERENCES
1> http://www.philologos.com
2> http://www.techreviwe.com
3> http://www.almaden.im.com
4> http://www.research.im.com
5> http://www.metropolismag.com
6> http://www.visuallee.com
7> http://www.howstuffworks.com
8> http://www.entecollege.com


CONTENTS

1) What is blue eyes?
2) Aims of blues eyes technology
3) Tracks used
4) Making computers see and feel
5) Affective computing
6) Blue eyes enabled devices
7) Software and hardware
8) Current scenario
9) Future
10) Conclusion
11) References

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11-02-2010, 08:11 PM
Post: #3
RE: Blue eyes (Download Full Report And Abstract)

.doc  Blue Eyes.doc (Size: 216.5 KB / Downloads: 1377)

ABSTRACT
Is it possible to create a computer which can interact with us as we interact each other For example imagine in a fine morning you walk on to your computer room and switch on your computer, and then it tells you "Hey friend, good morning you seem to be a bad mood today. And then it opens your mail box and shows you some of the mails and tries to cheer you. It seems to be a fiction, but it will be the life lead by "BLUE EYES" in the very near future.
The basic idea behind this technology is to give the computer the human power. We all have some perceptual abilities. That is we can understand each others feelings. For example we can understand ones emotional state by analyzing his facial expression. If we add these perceptual abilities of human to computers would enable computers to work together with human beings as intimate partners. The "BLUE EYES" technology aims at creating computational machines that have perceptual and sensory ability like those of human beings.
1 INTRODUCTION
Imagine yourself in a world where humans interact with computers. You are sitting in front of your personal computer that pan listen, talk, or even scream aloud. It has the ability to gather information about you and interact with you through special techniques like facial recognition, speech recognition, etc. It can even understand your emotions at the touch of the mouse. It verifies your identity, feels your presents, and starts interacting with you .You ask the computer to dial to your friend at his office. It realizes the urgency of the situation through the mouse, dials your friend at his office, and establishes a connection.
Human cognition depends primarily on the ability to perceive, interpret, and integrate audio-visuals and sensoring information. Adding extraordinary perceptual abilities to computers would enable computers to work together with human beings as intimate partners. Researchers are attempting to add more capabilities to computers that will allow them to interact like humans, recognize human presents, talk, listen, or even guess their feelings.
The BLUE EYES technology aims at creating computational machines that have perceptual and sensory ability like those of human beings. It uses non-obtrusige sensing method, employing most modern video cameras and microphones to identifies the users actions through the use of imparted sensory abilities . The machine can understand what a user wants, where he is looking at, and even realize his physical or emotional states.
2 EMOTION MOUSE
One goal of human computer interaction (HCI) is to make an adaptive, smart computer system. This type of project could possibly include gesture recognition, facial recognition, eye tracking, speech recognition, etc. Another non¬invasive way to obtain information about a person is through touch. People use their computers to obtain, store and manipulate data using their computer. In order to start creating smart computers, the computer must start gaining information about the user. Our proposed method for gaining user information through touch is via a computer input device, the mouse. From the physiological data obtained from the user, an emotional state may be determined which would then be related to the task the user is currently doing on the computer. Over a period of time, a user model will be built in order to gain a sense of the user's personality. The scope of the project is to have the computer adapt to the user in order to create a better working environment where the user is more productive. The first steps towards realizing this goal are described here.
2-1 EMOTION AND COMPUTING
Rosalind Picard (1997) describes why emotions are important to the computing community. There are two aspects of affective computing: giving the computer the ability to detect emotions and giving the computer the ability to express emotions. Not only are emotions crucial for rational decision making as Picard describes, but emotion detection is an important step to an adaptive computer system. An adaptive, smart computer system has been driving our efforts to detect a person's emotional state. An important element of incorporating emotion into computing is for productivity for a computer user. A study (Dryer & Horowitz, 1997) has shown that people with personalities that are similar or complement each other collaborate well. Dryer (1999) has also shown that people view their computer as having a personality. For these reasons, it is important to develop computers which can work well with its user.
By matching a person's emotional state and the context of the expressed emotion, over a period of time the person's personality is being exhibited. Therefore, by giving
the computer a longitudinal understanding of the emotional state of its user, the computer could adapt a working style which fits with its user's personality. The result of this collaboration could increase productivity for the user. One way of gaining information from a user non-intrusively is by video. Cameras have been used to detect a person's emotional state (Johnson, 1999). We have explored gaining information through touch. One obvious place to put sensors is on the mouse. Through observing normal computer usage (creating and editing documents and surfing the web), people spend approximately 1/3 of their total computer time touching their input device. Because of the incredible amount of time spent touching an input device, we will explore the possibility of detecting emotion through touch.
2.2 THEORY
Based on Paul Ekman's facial expression work, we see a correlation between a person's emotional state and a person's physiological measurements. Selected works from Ekman and others on measuring facial behaviors describe Ekman's Facial Action Coding System (Ekman and Rosenberg, 1997). One of his experiments involved participants attached to devices to record certain measurements including pulse, galvanic skin response (GSR), temperature, somatic movement and blood pressure. He then recorded the measurements as the participants were instructed to mimic facial expressions which corresponded to the six basic emotions. He defined the six basic emotions as anger, fear, sadness, disgust, joy and surprise. From this work, Dryer (1993) determined how physiological measures could be used to distinguish various emotional states
Six participants were trained to exhibit the facial expressions of the six basic emotions. While each participant exhibited these expressions, the physiological changes associated with affect were assessed. The measures taken were GSR, heart rate, skin temperature and general somatic activity (GSA). These zata were then subject to two analyses. For the first analysis, a multidimenslcr.s" scaling (MDS) procedure was used to determine the dimensionality of the data. Tr'a analysis suggested that the physiological similarities and dissimilarities of the six emotional states fit within a four dimensional model. For the second analysis, a
discriminant function analysis was used to determine the mathematic functions that would distinguish the six emotional states. This analysis suggested that all four
physiological variables made significant, nonredundant contributions to the functions that distinguish the six states. Moreover, these analyses indicate that these four physiological measures are sufficient to determine reliably a person's specific emotional state. Because of our need to incorporate these measurements into a small, non-intrusive form, we will explore taking these measurements from the hand. The amount of conductivity of the skin is best taken from the fingers. However, the other measures may not be as obvious or robust. We hypothesize that changes in the temperature of the finger are reliable for prediction of emotion. We also hypothesize the GSA can be measured by change in movement in the computer mouse. Our efforts to develop a robust pulse meter are not discussed here.
2.3 EXPERIMENTAL DESIGN
An experiment was designed to test the above hypotheses. The four physiological readings measured were heart rate, temperature, GSR and somatic movement. The heart rate was measured through a commercially available chest strap sensor. The temperature was measured with a thermocouple attached to a digital multimeter (DMM). The GSR was also measured with a DMM. The somatic movement was measured by recording the computer mouse movements.
2.3.1 Method
Six people participated in this study (3 male, 3 female). The experiment was within subject design and order of presentation was counter¬balanced across participants.
2.3.2 Procedure
Participants were asked to sit in front of the computer and hold the temperature and GSR sensors in their left hand hold the mouse with their right hand and wore the chest sensor. The resting (baseline) measurements were recorded for five minutes and then the participant was instructed to act out one emotion for five minutes. The emotions consisted of: anger, fear, sadness, disgust, happiness and surprise. The only instruction for acting out the emotion was to show the emotion in their facial expressions.
2.3.3 Results
The data for each subject consisted of scores for four physiological assessments [GSA, GSR, pulse, and skin temperature, for each of the six emotions (anger, disgust, fear, happiness, sadness, and surprise)] across the five minute baseline and test sessions. GSA data was sampled 80 times per second, GSR and temperature were reported approximately 3-4 times per second and pulse was recorded as a beat was detected, approximately 1 time per second. We first calculated the mean score for each of the baseline and test sessions. To account for individual variance in physiology, we calculated the difference between the baseline and test scores. Scores that differed by more than one and a half standard deviations from the mean were treated as missing. By this criterion, twelve score were removed from the analysis. The remaining data are described in Table 1.
In order to determine whether our measures of physiology could discriminate among the six different emotions, the data were analyzed with a discriminant function analysis. The four physiological difference scores were the discriminating variables and the six emotions were the discriminated groups. The variables were entered into the equation simultaneously, and four canonical discriminant functions were calculated. A Wilks' Lambda test of these four functions was marginally statistically significant; for lambda = .192, chi-square (20) = 29.748, p < .075. The functions are shown in Table 2
Table 2: Standardized Discriminant Function Coefficients.
Function
1 3 4
GSA 0.593 -0.926 0.674 0.033
GSR -0.664 0.957 0.350 0.583
Pulse 1.006 0.484 0.026 0.846
f emp. 1 277 0,405 0 423 -ft 293
The unstandardized canonical discriminant functions evaluated at group means are shown in Table 3. Function 1 is defined by sadness and fear at one end and anger and surprise at the other. Function 2 has fear and disgust at one end and sadness at the other. Function 3 has happiness at one end and surprise at the other. Function 4 has disgust and anger at one end and surprise at the other. Table 3:
To determine the effectiveness of these functions, we used them to predict the group membership for each set of physiological data. As shown in Table 4, two-thirds of the cases were successfully classified
Table 4: Classification Results.
Predicted Group Membership 1 Total 1
EMOTION Ancer Fear sadness hapf>iite sunwise
Original anuer 2 0 0 0 1 5
fear 0 0 \ * n 0 "»
¢'III IL'-> > i ¦i :. 1 i' 5
J v-ll -.1 :: 1 1 i 1 11 ¦> ¦\
i 1 1 :. ¦ i
;; i < i 1 ->
The results show the theory behind the Emotion mouse work is fundamentally sound. The physiological measurements were correlated to emotions using a correlation model. The correlation model is derived from a calibration process in which a baseline attribute-to emotion correlation is rendered based on statistical analysis of calibration signals generated by users having emotions that are measured or otherwise known at calibration time. Now that we have proven the method, the next step is to improve the hardware. Instead of using cumbersome multimeters to gather information about the user, it will be better to use smaller and less intrusive units. We plan to improve our infrared pulse detector which can be placed inside the body of the mouse. Also, a framework for the user modeling needs to be develop in order to correctly handle all of the information after it has been gathered. There are other possible applications for the Emotion technology other than just increased productivity for a desktop computer user. Other domains such as entertainment, health and the communications and the automobile industry could find this technology useful for other purposes.
3 MANUAL AND GAZE INPUT CASCADED (MAGIC) POINTING
This work explores a new direction in utilizing eye gaze for computer input. Gaze tracking has long been considered as an alternative or potentially superior pointing method for computer input. We believe that many fundamental limitations exist with traditional gaze pointing. In particular, it is unnatural to overload a perceptual channel such as vision with a motor control task.
We therefore propose an alternative approach, clubbed MAGIC (Manual And Gaze Input Cascaded) pointing. With such an approach, pointing appears to the user to be a manual task, used for fine manipulation and selection. However, a large portion of the cursor movement is eliminated by warping the cursor to the eye gaze area,
which encompasses the target. Two specific MAGIC pointing techniques, one conservative and one liberal, were designed, analyzed, and implemented with an eye tracker we developed. They were then tested in a pilot study. This early stage exploration showed that the MAGIC pointing techniques might offer many advantages, including reduced physical effort and fatigue as compared to traditional manual pointing, greater accuracy and naturalness than traditional gaze pointing, and possibly faster speed than manual pointing. The pros and cons of the two techniques are discussed in light of both performance data and subjective reports.
In our view, there are two fundamental shortcomings to the existing gaze pointing techniques, regardless of the maturity of eye tracking technology. First, given the one-degree size of the fovea and the subconscious jittery motions that the eyes constantly produce, eye gaze is not precise enough to operate Ul widgets such as scrollbars, hyperlinks, and slider handles In Proc. CHI'99: ACM Conference on Human Factors in Computing Systems. 246-253, Pittsburgh, 15-20 May1999 Copyright ACM 1999 0-201-48559-1/99/05...$5.00 on today's GUI interfaces. At a 25-inch viewing distance to the screen, one degree of arc corresponds to 0.44 in, which is twice the size of*a typical scroll bar and much greater than the size of a typical character.
Second, and perhaps more importantly, the eye, as one of our primary perceptual devices, has not evolved to be a control organ. Sometimes its movements are voluntarily controlled while at other times it is driven by external events. With the target selection by dwell time method, considered more natural than selection by blinking [7], one has to be conscious of where one looks and how long one looks at an object. If one does not look at a target continuously for a set threshold (e.g., 200 ms), the target will not be successfully selected. On the other hand, if one stares at an object for more than the set threshold, the object will be selected, regardless of the user's intention. In some cases there is not an adverse effect to a false target selection. Other times it can be annoying and counter¬productive (such as unintended jumps to a web page). Furthermore, dwell fee zsr only substitute for one mouse click. There are often two steps to target activation. A single click selects the target (e.g., an application icon) and a double click (or a different physical button click) opens the icon (e.g., launches an application). To perform both steps with dwell time is even more difficult. In short, to load the visual
perception channel with a motor control task seems fundamentally at odds with users' natural mental model in which the eye searches for and takes in information and the hand produces output that manipulates external objects. Other than for disabled users, who have no alternative, using eye gaze for practical pointing does not appear to be very promising.
Are there interaction techniques that utilize eye movement to assist the control task but do not force the user to be overly conscious of his eye movement We wanted to design a technique in which pointing and selection remained primarily a manual control task but were also aided by gaze tracking. Our key idea is to use gaze to
dynamically redefine (warp) the "home" position of the pointing cursor to be at the vicinity of the target, which was presumably what the user was looking at, thereby effectively reducing the cursor movement amplitude needed for target selection.
Once the cursor position had been redefined, the user would need to only make a small movement to, and click on, the target with a regular manual input device. In other words, we wanted to achieve Manual And Gaze Input Cascaded (MAGIC) pointing, or Manual Acquisition with Gaze Initiated Cursor. There are many different ways of designing a MAGIC pointing technique. Critical to its effectiveness is the identification of the target the user intends to acquire. We have designed two MAGIC pointing techniques, one liberal and the other conservative in terms of target identification and cursor placement. The liberal approach is to warp the cursor to every new object the user looks at (See Figure 1).
Inic larsjct will be within the circle with 95% probability
The cursor is warped to eye tracking position, which is on or near the true target
Previous cursor position, far from targe! (c 200 ni\e'ls i
I'i.L'inv I I IK- lilvrul M.V'IU point my kvhnk|iie cursor h placed in l he \icmii> ol a UIP.VI I hut I he user lixuk's "ii
The user can then take control of the cursor by hand near (or on) the target, or ignore it and search for the next target. Operationally, a new object is defined by sufficient distance (e.g., 120 pixels) from the current cursor position, unless the cursor is in a controlled motion by hand. Since there is a 120-pixel threshold, the cursor will not be warped when the user does continuous manipulation such as drawing. Note that this MAGIC pointing technique is different from traditional eye gaze control, where the user uses his eye to point at targets either without a cursor or with a cursor that constantly follows the jittery eye gaze motion.
The liberal approach may appear "pro-active," since the cursor waits readily in the vicinity of or on every potential target. The user may move the cursor once he decides to acquire the target he is looking at. On the other hand, the user may also feel that the cursor is over-active when he is merely looking at a target, although he may gradually adapt to ignore this behavior. The more conservative MAGIC pointing technique we have explored does not warp a cursor to a target until the manual input device has been actuated. Once the manual, input device has been actuated, the cursor is warped to the gaze area reported by the eye tracker. This area should be on or in the vicinity of the target. The user would then steer the cursor annually towards the target to complete the target acquisition. As illustrated in Figure 2, to minimize directional uncertainty after the cursor appears in the conservative technique, we introduced an "intelligent" bias. Instead of being
placed at the enter of the gaze area, the cursor position is offset to the intersection of the manual actuation vector and the boundary f the gaze area. This means that once warped, the cursor is likely to appear in motion towards the target, regardless of how the user actually actuated the manual input device. We hoped that with the intelligent bias the user would not have to Gaze position reported by eye tracker Eye tracking boundary with 95% confidence True target will be within the circle with 95% probability. The cursor is warped to eye tracking position, which is on or near the true target Previous cursor position, far from target (e.g., 200 pixels) Figure 1.
iepulis.<l K e\
li yet racking boundary with 95% confidence
Initial manual actuation vector
True turret <.vill be ¢A it h in the en ek w ill) '>"» pi olxihihu
The cursor is warped to the boundary of the gaze area, along the initial actuation vector
Previous cursor position* far from target
Figure 2. The conservative MAGIC pointing technique with "intelligent offset"
The liberal MAGIC pointing technique: cursor is placed in the vicinity of a target that the user fixates on. Actuate input device, observe the cursor position and decide in which direction to steer the cursor. The cost to this method is the increased manual movement amplitude. Figure 2. The conservative MAGIC pointing technique with "intelligent offset" To initiate a pointing trial, there are two strategies available to the user. One is to follow "virtual inertia:" move from tie cursor's current position towards the new target the user is looking at. This is likely the strategy the user will employ, due to the way the user interacts with today's interface. The alternative strategy, which may be more advantageous but takes time to learn, is to ignore the previous cursor position and make a motion which is most convenient and least effortful to the user for a given input device.
The goal of the conservative MAGIC pointing method is the following. Once the user looks at a target and moves the input device, the cursor will appear "out of the blue" in motion towards the target, on the side of the target opposite to the initial actuation vector. In comparison to the liberal approach, this conservative approach has both pros and cons. While with this technique the cursor would never be over-active and jump to a place the user does not intend to acquire, it may require more hand-eye coordination effort. Both the liberal and the conservative MAGIC pointing techniques offer the following potential advantages:
1. Reduction of manual stress and fatigue, since the cross
screen long-distance cursor movement is eliminated from manual control.
2. Practical accuracy level. In comparison to traditional pure gaze pointing whose accuracy is fundamentally limited by the nature of eye movement, the MAGIC pointing techniques let the hand complete the pointing task, so they can be as accurate as any other manual input techniques.
3. A more natural mental model for the user. The user does not have to be aware of the role of the eye gaze. To the user, pointing continues to be a manual task, with a cursor conveniently appearing where it needs to be.
4. Speed. Since the need for large magnitude pointing operations is less than with pure manual cursor control, it is possible that MAGIC pointing will be faster than pure manual pointing.
5. Improved subjective speed and ease-of-use. Since the manual pointing amplitude is smaller, the user may perceive the MAGIC pointing system to operate faster and more pleasantly than pure manual control, even if it operates at the same speed or more slowly.
The fourth point wants further discussion. According to the well accepted Fitts' Law, manual pointing time is logarithmically proportional to the A/W ratio, where A is the movement distance and W is the target size. In other words, targets which are smaller or farther away take longer to acquire.
For MAGIC pointing, since the target size remains the same but the cursor movement distance is shortened, the pointing time can hence be reduced. It is less clear if eye gaze control follows Fitts' Law. In Ware and Mikaelian's study, selection time was shown to be logarithmically proportional to target distance, thereby conforming to Fitts' Law. To the contrary, Silbert and Jacob [9] found that trial completion time with eye tracking input increases little with distance, therefore defying Fitts' Law. In addition to problems with today's eye tracking systems, such as delay, error, and inconvenience, there may also be many potential human factor disadvantages to the MAGIC pointing techniques we have proposed, including the following:
1. With the more liberal MAGIC pointing technique, the cursor warping can be overactive at times, since the cursor moves to the new gaze location whenever the eye gaze moves more than a set distance (e.g., 120 pixels) away from the cursor. This could be particularly distracting when the user is trying to read. It is possible to introduce additional constraint according to the context. For example, when the user's eye appears to follow a text reading pattern, MAGIC pointing can be automatically suppressed.
2. With the more conservative MAGIC pointing technique, the uncertainty of the exact location at which the cursor might appear may force the user, especially a novice, to adopt a cumbersome strategy: take a touch (use the manual input device to activate the cursor), wait (for the cursor to appear), and move (the cursor to the target manually). Such a strategy may prolong the target acquisition time. The user may have to learn a novel hand-eye coordination pattern to be efficient with this technique. Gaze position reported by eye tracker Eye tracking boundary with 95% confidence True target will be within the circle with 95% probability The cursor is warped to the boundary of the gaze area, along the initial actuation vector Previous cursor position, far from target Initial manual actuation vector
3. With pure manual pointing techniques, the user, knowing the current cursor location, could conceivably perform his motor acts in parallel to visual search. Motor action may start as soon as the user's gaze settles on a target. With MAGIC pointing techniques, the motor action computation (decision) cannot start until the cursor appears. This may negate the time saving gained from the MAGIC pointing technique's reduction of movement amplitude. Clearly, experimental (implementation and empirical) work is needed to validate, refine, or invent alternative MAGIC pointing techniques.
3.1 IMPLEMENTATION
We took two engineering efforts to implement the MAGIC pointing techniques. One was to design and implement an eye tracking system and the other was to implement MAGIC pointing techniques at the operating systems level, so that the techniques can work with all software applications beyond "demonstration" software.
3.2 THE IBM ALMADEN EYE TRACKER
Since the goal of this work is to explore MAGIC pointing as a user interface technique, we started out by purchasing a commercial eye tracker (ASL Model 5000) after a market survey. In comparison to the system reported in early studies (e.g. [7]), this system is much more compact and reliable. However, we felt that it was still not robust enough for a variety of people with different eye characteristics, such as pupil brightness and correction glasses. We hence chose to develop and use our own eye tracking system [10]. Available commercial systems, such as those made by ISCAN Incorporated, LC Technologies, and Applied Science Laboratories (ASL), rely on a single light source that is positioned either off the camera axis in the case of the ISCANETL-400 systems, or on-axis in the case of the LCT and the ASL E504 systems. Illumination from an off-axis source (or ambient illumination) generates a dark pupil image.
When the light source is placed on-axis with the camera optical axis, the camera is able to detect the light reflected from the interior of the eye, and the image of the pupil appears bright (see Figure 3).
This effect is often seen as the red-eye in flash photographs when the flash is close to the camera lens.
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14
Bright (left) and dark (right) pupil images resulting from on- and off-axis illumination. The glints, or corneal reflections, from the on- and off-axis light sources can be easily identified as the bright points in the iris. The Almaden system uses two near infrared (IR) time multiplexed light sources, composed of two sets of IR LED's, which were synchronized with the camera frame rate. One light source is placed very close to the camera's optical axis and is synchronized with the even frames. Odd frames are synchronized with the second light source, positioned off axis. The two light sources are calibrated to provide approximately equivalent whole-scene illumination. Pupil detection is realized by means of subtracting the dark pupil image from the bright pupil image. After thresholding the difference, the largest connected component is identified as the pupil. This technique significantly increases the robustness and reliability of the eye tracking system. After implementing our system with satisfactory results, we* discovered that similar pupil detection schemes had been independently developed by Tomonoetal and Eb'isawa and Satoh.
It is unfortunate that such a method has not been used in the commercial systems. We recommend that future eye tracking product designers consider such an approach.
Once the pupil has been detected, the corneal reflection (the glint reflected from the surface of the cornea due to one of the light sources) is determined from the dark pupil image. The reflection is then used to estimate the user's point of gaze in terms of the screen coordinates where the user is looking at. The estimation of the user's gaze requires an initial calibration procedure, similar to that required by commercial eye trackers. Our system operates at 30 frames per second on a Pentium II 333 MHz machine running Windows NT. It can work with any PCI frame grabber compatible with Video for Windows.
3.3 IMPLIMENTING MAGIC POINTING
We programmed the two MAGIC pointing techniques on a Windows NT system. The techniques work independently from the applications. The MAGIC pointing program takes data from both the manual input device (of any type, such as a mouse) and the eye tracking system running either on the same machine or on another machine connected via serial port. Raw data from an eye tracker can not be directly used for gaze-based interaction, due to noise from image processing, eye movement jitters, and samples taken during saccade (ballistic eye movement) periods. We experimented with various filtering techniques and found the most effective filter in our case is similar to that described in [7]. The goal of filter design in general is to make the best compromise between preserving signal bandwidth and eliminating unwanted noise. In the case of eye tracking, as Jacob argued, eye information relevant to interaction lies in the fixations. The key is to select fixation points with minimal delay. Samples collected during a saccade are unwanted and should be avoided. In designing our algorithm for picking points of fixation, we considered our tracking system speed (30 Hz), and that the MAGIC pointing techniques utilize gaze information only once for each new target, probably immediately after a saccade. Our filtering algorithm was designed to pick a fixation with minimum delay by means of selecting two adjacent points over two samples.
3.4 EXPERIMENT
Empirical studies, are relatively rare in eye tracking-based interaction research, although they are particularly needed in this field. Human behavior and processes at the perceptual motor level often do not conform to conscious-level reasoning. One usually cannot correctly describe how to make a turn on a bicycle. Hypotheses on novel interaction techniques can only be validated by empirical data. However, it is also particularly difficult to conduct empirical research on gaze-based interaction techniques, due to the complexity of eye movement and the lack of reliability in eye tracking equipment. Satisfactory results only come when "everything is going right." When results are not as expected, I: Confused difficult to find the true reason among many possible reasons: Is it because a subject's particular eye property fooled the eye tracker Was there a calibration error Or random noise in the imaging system Or is the hypothesis in fact invalid We are still at a very early stage of exploring the MAGIC pointing techniques. More refined or even very different techniques may be designed in the future. We are by no means ready to conduct the definitive empirical studies on MAGIC pointing. However, we also feel that it is important to subject our work to empirical evaluations early so that quantitative observations can be made and fed back to the iterative design-evaluation-design cycle. We therefore decided to conduct a small-scale pilot study to take an initial peek at the use of MAGIC pointing, however unrefined.
3.5 EXPERIMENTAL DESIGN
The two MAGIC pointing techniques described earlier were put to test using a set of parameters such as the filter's temporal and spatial thresholds, the minimum cursor warping distance, and the amount of "intelligent bias" (subjectively selected by the authors without extensive user testing). Ultimately the MAGIC pointing techniques should be evaluated with an array of manual input devices, against both pure manual and pure gaze-operated pointing methods.
Since this is an early pilot study, we decided to limit ourselves to one manual input device. A standard mouse was first ^considered to be the manual input device in the experiment. However, it was soon realized not to be the most suitable device for MAGIC pointing, especially when a user decides to use the push-upwards strategy with the intelligent offset. Because in such a case the user always moves in one direction, the mouse tends to be moved off the pad, forcing the user adjust the mouse position, often during a pointing trial. We hence decided to use a miniature isometric pointing stick (IBM Track Point IV, commercially used in the IBM ThinkPad 600 and 770 series notebook computers). Another device suitable for MAGIC pointing is a touchpad: the user can choose one convenient gesture and to take advantage of the intelligent offset. The experimental task was essentially a Fitts' pointing task. Subjects were asked to point and click at targets appearing in random order. If the subject clicked off-target, a miss was logged but the trial continued until a target was clicked. An extra trial was added to make up for the missed trial. Only trials with no misses were collected for time performance analyses. Subjects were difficult to find the true reason among many possible reasons: Is it because a subject's particular eye property fooled the eye tracker Was there a calibration error Or random noise in the imaging system Or is the hypothesis in fact invalid We are still at a very early stage of exploring the MAGIC pointing techniques. More refined or even very different techniques may be designed in the future. We are by no means ready to conduct the definitive empirical studies on MAGIC pointing. However, we also feel that it is important to subject our work to empirical evaluations early so that quantitative observations can be made and fed back to the iterative design-evaluation-design cycle. We therefore decided to conduct a small-scale pilot study to take an initial peek at the use of MAGIC pointing, however unrefined.
3.5 EXPERIMENTAL DESIGN
The two MAGIC pointing techniques described earlier were put to test using a set of parameters such as the filter's temporal and spatial thresholds, the minimum cursor warping distance, and the amount of "intelligent bias" (subjectively selected by the authors without extensive user testing). Ultimately the MAGIC pointing techniques should be evaluated with an array of manual input devices, against both pure manual and pure gaze-operated pointing methods.
Since this is an early pilot study, we decided to limit ourselves to one manual input device. A standard mouse was first ^considered to be the manual input device in the experiment. However, it was soon realized not to be the most suitable device for MAGIC pointing, especially when a user decides to use the push-upwards strategy with the intelligent offset. Because in such a case the user always moves in one direction, the mouse tends to be moved off the pad, forcing the user adjust the mouse position, often during a pointing trial. We hence decided to use a miniature isometric pointing stick (IBM Track Point IV, commercially used in the IBM ThinkPad 600 and 770 series notebook computers). Another device suitable for MAGIC pointing is a touchpad: the user can choose one convenient gesture and to take advantage of the intelligent offset. The experimental task was essentially a Fitts' pointing task. Subjects were asked to point and click at targets appearing in random order. If the subject clicked off-target, a miss was logged but the trial continued until a target was clicked. An extra trial was added to make up for the missed trial. Only trials with no misses were collected for time performance analyses. Subjects were
asked to complete the task as quickly as possible and as accurately as possible. To serve as a motivator, a $20 cash prize was set for the subject with the shortest mean session completion time with any technique.
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Figure 4. Kxpcrirnental task: point at paired targets
The task was presented on a 20 inch CRT color monitor, with a 15 by 11 inch viewable area set at resolution of 1280 by 1024 pixels. Subjects sat from the screen at a distance of 25 inches. The following factors were manipulated in the experiments:
. two target sizes: 20 pixels (0.23 in or 0.53 degree of viewing angle at 25 in distance) and 60 pixels in diameter (0.7 in, 1.61 degree)
three target distances: 200 pixels (2.34 in, 5.37 degree), 500 pixels (5.85 in, 13.37 degree), and 800 pixels (9.38 in, 21.24 degree)
three pointing directions: horizontal, vertical and diagonal
A within-subject design was used. Each subject performed the task with all three techniques: (1) Standard, pure manual pointing with no gaze tracking (No Gaze); (2) The conservative MAGIC pointing method with intelligent offset (Gazel); (3) The liberal MAGIC pointing method (Gaze2). Nine subjects, seven male and two female, completed the experiment. The order of techniques was balanced by a Latin square pattern. Seven subjects were experienced Track Point users, while two had little or no experience. With each technique, a 36-trial practice session was first given, during which subjects were encouraged to explore and
to find the most suitable strategies (aggressive, gentle, etc.). Tre practice session was followed by two data collection sessions. Although our e.z
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tracking system allows head motion, at least for those users who do not wear glasses, we decided to use a chin rest to minimize instrumental error.
3.6 EXPERIMENTAL RESULTS
Sesslonl
Given the pilot nature and the small scale of the experiment, we expected the statistical power of the results to be on the weaker side. In other words, while the significant effects revealed are important, suggestive trends that are statistically non-significant are still worth noting for future research. First, we found that subjects' trial completion time significantly varied with techniques: F(2, 16) = 6.36, p< 0.01.
Session2
Figure 5. Mean completion time (sec) vs. experiment session
The total average completion time was 1.4 seconds with the standard manual control technique 1.52 seconds with the conservative MAGIC pointing technique (Gazel), and 1.33 seconds with the liberal MAGIC pointing technique (Gaze2). Note that the Gazel
Technique had the greatest improvement from the first to the second experiment session, suggesting the possibility of matching the performance of the other two techniques with further practice.
As expected, target size significantly influenced pointing time: f(1,8) = 178, p < 0.001. This was true for both the manual and the two MAGIC pointing techniques (Figure 6).
Pointing amplitude also significantly affected completion time: F(2, 8) = 97.5, p < 0.001. However, the amount of influence varied with the technique used, as indicated by the significant interaction between technique and amplitude: F(4, 32) = 7.5, p < 0.001 (Figure 7).
As pointing amplitude increased from 200 pixels to 500 pixels and then to 800 pixels, subjects' completion time with the No_Gaze condition increased in a non-linear, logarithmic-like pace as Fitts' Law predicts. This is less true with the
two MAGIC pointing techniques, particularly the Gaze2 condition, which is definite:, not logarithmic. Nonetheless, completion time with the MAGIC pointing techniques did increase as target distance increased. This is intriguing because in MAGIC pointing techniques, the manual control portion of the movement should be the distance from the warped cursor position to the true target. Such distance depends on eye tracking system accuracy, which is unrelated to the previous cursor position.
In short, while completion time and target distance with the MAGIC pointing techniques did not completely follow Fitts' Law, they were not completely independent either. Indeed, when we lump target size and target distance according to the Fitts' Law
Index of Difficulty ID = \og2(A/W+ 1) [15], we see a similar phenomenon. For the No_Gaze condition:
7=0.28 + 0.31 ID (^=0.912) The particular settings of our experiment were very different from those typically reported in a Fitts' Law experiment: to simulate more realistic tasks we used circular targets distributed in varied directions in a randomly shuffled order, instead of two vertical bars displaced only in the horizontal dimension. We also used an isometric pointing stick, not a mouse. Considering these factors, the above equation is reasonable. The index of performance {IP) was 3.2 bits per second, in comparison to the 4.5 bits per second in a typical setting (repeated mouse clicks on two vertical bars) [16].
For the Gazel condition:
7=0.8 + 0.22 ID (^=0.716) IP = 4.55 bits per second
For Gaze2:
7=0.6 + 0.21 ID (^=0.804) IP = 4.76 bits per second
Note that the data from the two MAGIC pointing techniques fit the Fitts' Law model relatively poorly (as expected), although the indices of performance (4.55 and 4.76 bps) were much higher than the manual condition (3.2 bps).
Finally, Figure 8 shows that the angle at which the targets were presented had little influence on trial completion time: F(2 16) = 1.57, N.S.
1.6 1.4 I 2
1
Horizontal Diagonal Veitioal
O No_Gaze Q Gazel p Gaze2
Figure 8. Mean completion time (sec) vs. target angle (degrees)
The number of misses (clicked off target) was also analyzed. The only significant factor to the number of misses is target size: F(1,8) = 15.6, p < 0.01. Users tended to have more misses with small targets. More importantly, subjects made no more misses with the MAGIC pointing techniques than with the pure manual technique (No_Gaze - 8.2 %, Gazel -7%, Gaze2 - 7.5%).
4 ARTIFICIAL INTELLIGENT SPEECH RECOGNITION
It is important to consider the environment in which the speech recognition system has to work. The grammar used by the speaker and accepted by the system, noise level, noise type, position of the microphone, and speed and manner of the user's speech are some factors that may affect the quality of speech recognition .When you dial the telephone number of a big company, you are likely to hear the sonorous voice of a cultured lady who responds to your call with great courtesy saying "Welcome to company X. Please give me the extension number you want". You pronounce the extension number, your name, and the name of person you want to contact. If the called person accepts the call, the connection is given quickly. This is artificial intelligence where an automatic call-handling system is used without employing any telephone operator.
4.1 THE TECHNOLOGY
Artificial intelligence (Al) involves two basic ideas. First, it involves studying the thought processes of human beings. Second, it deals with representing those processes via machines (like computers, robots, etc). Al is behavior of a machine, which, if performed by a human being, would be called intelligent. It makes machines smarter and more useful, and is less expensive than natural intelligence. Natural language processing (NLP) refers to artificial intelligence methods of communicating with a computer in a natural language like English. The main objective of a NLP program is to understand input and initiate action. The input words are scanned and matched against internally stored known words. Identification of a key word causes some action to be taken. In this way, one can communicate with the computer in one's language. No special commands or computer language are required. There is no need to enter programs in a special language for creating software.
4.2 SPEECH RECOGNITION
The user speaks to the computer through a microphone, which, in used; a simple system may contain a minimum of three filters. The more the number of filters used, the higher the probability of accurate recognition. Presently, switched capacitor digital filters are used because these can be custom-built in integrated circuit form. These are smaller and cheaper than active filters using operational amplifiers. The filter output is then fed to the ADC to translate the analogue signal into digital word. The ADC samples the filter outputs many times a second. Each sample represents different amplitude of the signal .Evenly spaced vertical lines represent the amplitude of the audio filter output at the instant of sampling. Each value is then converted to a binary number proportional to the amplitude of the sample. A central processor unit (CPU) controls the input circuits that are fed by the
ADCS. A large RAM (random access memory) stores all the digital values in a buffer area. This digital information, representing the spoken word, is now accessed by the CPU to process it further. The normal speech has a frequency range of 200 Hz to 7 kHz. Recognizing a telephone call is more difficult as it has bandwidth limitation of 300 Hz to3.3 kHz.
As explained earlier, the spoken words are processed by the filters and ADCs. The binary representation of each of these words becomes a template or standard, against which the future words are compared. These templates are stored in the memory. Once the storing process is completed, the system can go into its active mode and is capable of identifying spoken words. As each word is spoken, it is converted into binary equivalent and stored in RAM. The computer then starts searching and compares the binary input pattern with the templates, t is to be noted that even if the same speaker talks the same text, there are always slight variations in amplitude or loudness of the signal, pitch, frequency difference, time gap, etc. Due to this reason, there is never a perfect match between the template and binary input word. The pattern matching process therefore uses statistical techniques and is designed to look for the best fit.
The values of binary input words are subtracted from the corresponding values in the templates. If both the values are same, the difference is zero and there is perfect match. If not, the subtraction produces some difference or error. The smaller the error, the better the match. When the best match occurs, the word is identified and displayed on the screen or used in some other manner. The search process takes a considerable amount of time, as the CPU has to make many comparisons before recognition occurs. This necessitates use of very high-speed processors. A large RAM is also required as even though a spoken word may last only a few hundred milliseconds, but the same is translated into many thousands of digital words. It is important to note that alignment of words and templates are to be matched correctly in time, before computing the similarity score. This process, termed as dynamic time warping, recognizes that different speakers pronounce the same words at different speeds as well as elongate different parts of the same word. This is important for the speaker-independent recognizers.
4.3 APPLICATIONS
One of the main benefits of speech recognition system is that it lets user do other works simultaneously. The user can concentrate on observation and manual operations, and still control the machinery by voice input commands. Another major application of speech processing is in military operations. Voice control of weapons is an example. With reliable speech recognition equipment, pilots can give commands and information to the computers by simply speaking into their microphones”they don't have to use their hands for this purpose. Another good example is a radiologist scanning hundreds of X-rays, ultrasonograms, CT scans and simultaneously dictating conclusions to a speech recognition system connected to word processors. The radiologist can focus his attention on the images rather than writing the text. Voice recognition could also be used on computers for making airline and hotel reservations. A user requires simply to state his needs, to make reservation, cancel a reservation, or make enquiries about schedule.
5 THE SIMPLE USER INTERST TRACKER (SUITOR)
Computers would have been much more powerful, had they gained perceptual and sensory abilities of the living beings on the earth. What needs to be developed is an intimate relationship between the computer and the humans. And the Simple User Interest Tracker (SUITOR) is a revolutionary approach in this direction.
By observing the Webpage a netizen is browsing, the SUITOR can help by fetching more information at his desktop. By simply noticing where the user's eyes focus on the computer screen, the SUITOR can be more precise in determining his topic of interest. It can even deliver relevant information to a handheld device. The success lies in how much the suitor can be intimate to the user. IBM's BlueEyes research project began with a simple question, according to Myron Flickner, a manager in Almaden's USER group: Can we exploit nonverbal cues to create more effective user interfaces
One such cue is gaze”the direction in which a person is looking. Flickner and his colleagues have created some new techniques for tracking a person's eyes and have incorporated this gaze-tracking technology into two prototypes. One, called SUITOR (Simple User Interest Tracker), fills a scrolling ticker on a computer screen with information related to^the user's current task. SUITOR knows where you are looking, what applications you are running, and what Web pages you may be browsing. "If I'm reading a Web page about IBM, for instance," says Paul Maglio, the Almaden cognitive scientist who invented SUITOR, "the system presents the latest stock price or business news stories that could affect IBM. If I read the headline off the ticker, it pops up the story in a browser window. If I start to read the story, it adds related stories to the ticker. That's the whole idea of an attentive system”one that attends to what you are doing, typing, reading, so that it can attend to your information needs."
6 CONCLUSION
The nineties witnessed quantum leaps interface designing for improved man machine interactions. The BLUE EYES technology ensures a convenient way of simplifying the life by providing more delicate and user friendly facilities in computing devices. Now that we have proven the method, the next step is to improve the hardware. Instead of using cumbersome modules to gather information about the user, it will be better to use smaller and less intrusive units. The day is not far when this technology will push its way into your house hold, making you more lazy. It may even reach your hand held mobile device. Any way this is only a technological forecast.
7 BIBILIOGRAPHY
Ekman, P. and Rosenberg, E. (Eds.) Cl9a7)i What the Face Reveals: Basic and Applied
Studies of Spontaneous Expression Using the Facial Action Coding System (FACS| Oxford
University Press: New York.
Dryer, D.C. (1993). Multidimensional and Discriminant Function Analyses of Affective State
Data. Stanford University, unpublished manuscript.
Dryer, D.C. (1999). Getting personal with computers: How to design personalities for agents.
Applied Artificial Intelligence
Dryer, D.C, and Horowitz, L.M. (1997). When do opposites attract Interpersonal
Complementarity versus similarity. Journal of Personality and Social Psychology
Johnson, R.C. (1999). Computer Program Recognizes Facial Expressions. EE
Times '
http://www.eetimes.com, April 5.
Picard, R. (1997). Affective Computing. MIT Press: Cambridge.
CONTENTS
1. INTRDUCTION 1
2. EMOTION MOUSE 2
2.1 EMOTION AND COMPUTING 2
2.2THEORY 3
2.3EXPERIMENTAL DESIGN 4
2.3.1 METHOD 4
2.3.2 PROCEDURE 5
2.3.3 RESULTS 5
3. MANUAL AND GAZE INPUT CASCADED 7
3.1 IMPLEMENTATION 14
3.2 IBM ALMADEN EYE TRACKER 14
3.3 IMPLIMENTING MAGIC POINTING 16
3.4 EXPERIMENT 16
3.5 EXPERIMENTAL DESIGN 17
3.6 EXPERIMENTAL RESULTS 19
4. ARTIFICIAL INTELLIGENT SPEECH RECOGNITION 22
4.1 THE TECHNOLOGY” 23
4.2 SPEECH RECOGNITION 23
4.3 APPLICATIONS 24
5. THE SIMPLE USER INTEREST TRACKER 26
6. CONCLUSION 27
7 .BIBILIOGRAPHY 28
12-02-2010, 11:50 AM
Post: #4
RE: Blue eyes (Download Full Report And Abstract)

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ABSTRACT
The computers we use in our day-to-day life have tremendous abilities to sophisticated tasks easily.They can do the tasks assigned to them with a lightning speed. They can understand a variety of computer languages. They can effectively compile the programs written in these languages and understand what to do.
Despite their lightning speed and awesome powers of computation, today's PCs are essentially deaf, dump and blind. If wc want our computers to be genuinely intelligent and interact naturally with us, we must give them the power to recognize and understand emotions.
Imagine ourselves in a world where human interact with computers in the same way as with humans. It has the ability to gather information about us and interact accordingly. It can even understand the emotions of it's user at the touch of a mouse. This can be brought into reality with the help of the upcoming technology The BLUE EYES.
For the implementation of The Blue Eyes technology we must give our personnel computers the power to recognize and understand our emotions. The search for a unique and reliable technique for securing resources has finally led to the realization that human physiological traits are unique enough for it to be used as an identifier. The thing lacking was the technology to exploit the potential of these characteristics. The new technology, BIOMETRICS emerged as a solution for this situation. Thus BLUE EYES used biometric sensors can make incredible effect in the field of emotion detection which itself can act as a security measure.
1. INTRODUCTION
The BLUE EYES project was started at IBM's Almaden Research Centre in USA. It aims at giving computers highly developed abilities to perceive, integrate and interpret visual, auditory and touch information. The project explores various ways of allowing people to operate computers without conscious effort. Gaze tracking seemed like the natural place to start. This is because the eyes are the most expressive part in a human being and all the emotions are reflected in the eyes.
These days it is the humans who have to adapt to the computers by learning various languages, modes of operations etc. The BLUE EYES project aims at creating computers, which can adapt to humans and thus enable them to operate much more conveniently. The project enables computers and humans to work together more as partners.
Effective utilization of existing Biometric techniques for the purpose of the emotion detection is done in the Blue Eyes project. Different Biometric sensors are used to monitor different parts of the human body. By proper processing of the output of these sensors the emotion of a person is identified. The Blue Eyes uses non-obtrusive sensing technologies such as video cameras and microphones, to identify user's action and to extract key information. These clues are analyzed to determine the user's physical, emotional or informational state.
2. BIOMETRICS
The computers must be given power to sense emotion of its user, in order to make them 'attentive computers'. BIOMETRICS is the science used for the implementation. It is the science by which we measure the physiological and behavioral characteristics of a person. And by using these characteristics the emotional state of the user is identified.
The eyes are the most expressive part of a human being. So iris scanning is the most important technique used. The emotions of a human being will directly reflect in his physiological attributes. So the measure of heartbeat, blood pressure etc. will give straight information about the emotional state of the user. Several techniques like eye gaze tracking, facial expression detection, speech recognition, detection using Emotion mouse, Jazz multi sensor etc. are used for emotion detection.
Besides the emotion detection, the Biometric techniques can be used for security purposes. The physical characteristics like the finger prints, hand geometry, retina, voice etc. are unique for every human being. Thus by analyzing these characteristics a person can be easily identified. Thus several methods like fingerprint identification, retinal scan, voice identification etc are successfully implemented for the purpose of security. Here in the implementation of Blue Eyes, the biometric techniques are used mainly for the purpose of emotion detection.

3. MAIN BIOMETRIC TECHNIQUES
3.1 Facial expression detection
The facial expression detection consists of two steps - The Face recognition and Expression detection.
Face Recognition
Face recognition is applied in a variety of domains, predominantly for security. The user's face must be identified before further processing. The first problem to be solved before attempting face recognition is to find the face in the image. The first stage of the process is color segmentation, which simply determines if the proportion of the skin tone pixels is greater than some threshold. Subsequently candidate regions are given scores.Next instead of searching for all the facial features directly in the face image, a few 'high level' features (eyes, nose, mouth) are first located. Then other 26 'low level' features that may be parts of eyes, nose, mouth, eyebrows etc. are located with respect to high level feature locations. The approximate locations of the high level features are known from statistics of mean and variance relative to the nose position, gathered on the training database
Expression Detection
The facial expression determination is a field in which fast researches are going on. The most intriguing invention is Expression Glasses. This is a mobile device which can be worn by the user. This is very comfortable to wear and the survey about it among the subject users provide a good result. This device measures the movement of face muscles. The movement is then compared with some reference index to determine emotion.
This device is used to determine the user's level of interest or confusion by measuring the movement of muscles around the eyes. The output from the expression

glasses is fed to the computer for further processing. There it is converted into a two colored bar graph, in which red bars indicate confusion and green for interest. This graph will give a clear indication about the level of interest or confusion.
3.2 Speech Recognition
Speech recognition is the process of converting a speech signal to a set of words, by means of an algorithm implemented as computer program. Voice or speaker identification is a related process that attempts to identify the person speaking, as opposed to what is being said.
Speech is processed by means of complex voice processing algorithms. First the speech signal is converted into a set of words, by proper sampling, quantization and coding. These words are called voice prints. There is already a reference index which contains different voice prints corresponding to each emotion. By comparing the subject user's voice print with these reference the emotion is identified. Mainly the tone of the voice is compared, besides what is being said.
3.3 Emotion Mouse
A non-invasive way to obtain the information about the user's emotional state is through touch. People use their computer to store and manipulate data. The proposed method for obtaining user information through touch is via a computer input device, the mouse. The computer determines the user's emotional state by a simple touch. Sensors in the mouse sense physiological attributes, which are correlated to emotions using a correlation model.
The emotion mouse consists of a number of sensors which will sense individual attributes. The different sensors incorporated in the emotion mouse are IR sensor, thermosister chip, galvanic sensor. The IR sensor will measure the heartbeat from the fingertip, the thermosister chip will measure the body temperature and galvanic sensor will take the measure of skin conductivity. All these attributes are combined to form a vector which is the representative of the emotional state of the user.
This vector is used to determine the present emotion of the user. This vector is being compared with the emotion-to- attribute correlation model and the emotion detection is realized.

3.4 Eye Gaze Tracking
The eye gaze tracking system monitors the eye movement of the user to detect his emotional state. This system uses a technique called Pupil finder to monitor subject user's eye movement.
3.5 Jazz Multi Sensor
Jazz multi sensor is a sensor which will sense multi attributes. This sensor is a mobile device that the user can wear it on his forehead. This device is a marvelous one which has multi sensors incorporated on it. This device uses all the techniques of emotion detection described above. This single device is capable of detecting a subject user's emotional state. This device is developed at the research laboratory of Poznan University, Poland.

The different sensors in a Jazz sensor are IR sensor, oculographic transducer, environment illumination sensor, expression glass, microphone etc. The different sensors senses different physiological attributes which will give a complex output.
The plethysmographic signals, which are the signals from cardiac, circulatory and pulmonary systems, will give direct indication of the emotional state. The sensors sensing these signals are collectively known as plethysmograhic transducers. The IR sensor senses the heartbeat and level of blood oxygenation. The heartbeat pulse rate is calculated at the analysis section by making use of the level of oxyhaemoglobin and de-oxyhaemoglobin sensed by the Jazz sensor.
The voice data is sensed by microphone. The audio signal corresponding to this voice data is properly coded and transmitted to the analysis unit. The expression glasses will monitor the muscle movement around eyes, and the facial expression of the subject user is examined.
The saccades are the most abrupt eye movement It will directly give indication about level of visual attention of the user. The eye movement is monitored by oculographic transducers. The implement the principle eye gaze tracking. The two axial accelerometers provided will sense the velocity of eye motion along with head acceleration. The sensor provided for detecting eye movement are optical transducers. So for getting accurate processed data about eye position and eye movement ,we need some information about the operating room. The environmental illumination sensor will serve this purpose. It will provide data regarding the light conditions of the room.
The Jazz sensor thus senses different physiological attributes which will point to the emotional state of the user. The separate attributes will have different values and hence they have to be appropriately multiplexed. The Jazz sensor will take measure of saccadic activity in an interval of every lKHz and other parameters at an interval of 250Hz. Thus they are properly transmitted for suitable analysis to acquire emotion detection.
4. BLUE EYES HARDWARE
4.1 System Overview
Blue eyes system provides technical means for monitoring and recording the operator's physiological parameters . The Blue Eyes hardware consists of mainly two parts. They are the Data Acquisition Unit and The Central System Unit.
Data Acquisition Unit is a mobile measuring device, which consists of a number of modules like physiological parameter sensor, voice interface, ID card interface etc. ID card assigned to each of the operators and adequate user profdes on the central system unit provide necessary data personalization, so different people can use a single mobile device. The mobile device is integrated with Bluetooth module for providing wireless interface between sensors worn by the operator and the central unit.
Central System Unit actually provides the real-time buffering of incoming sensor signals and semi-real-time processing of the data. It consists of different data modules for the proper functioning . The overall system diagram is shown below. The explanation for individual components is as follows.

Overall picture diagram

4.2 Data Acquisition Unit
Data Acquisition Unit is the mobile part of the Blue Eyes hardware. Main tasks of mobile Data Acquisition Unit are to maintain Bluetooth connections, receive the information from the sensor and sending it over the wireless connection, to deliver the alarm messages send from the central system unit to the operator and handle personalized ID cards.
The components which constitute the data acquisition unit are Atmel 89C52, a PCM Codec, personal ID card interface, Jazz multisensor interface, a beeper, an LCD display, LED indicators a simple keyboard, and finally a Bluettoth modue. The arrangement of these blocks to realize a DAU is picturized below.

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The DAU of the Blue Eyes hardware composed of the different components as explained below.
a. Personnel ID card interface.
ID cards are assigned to each of the operators and corresponding adequate user profiles on the central system unit provides necessary data personalization, so different people can use the single mobile DAU. In order to start the proper functioning of the DAU the operator should insert the personal ID card. After inserting the ID card into the mobile device and entering proper PIN code, the device will start listening for incoming Bluetooth connections. Once the connection has been established and authorization process is succeeded (PIN code is correct) central system start monitoring the output of the DAU.
b. Jazz multi sensor
The multi sensor will sense different physiological parameters and send the complex output signal to DAU. This signal is received, properly coded and send to the central system unit over the Bluetooth connection. The Jazz sensor is properly interfaced to the microcontroller.
c. Bluetooth module
The ROK 101008 Bluetooth module is used here to establish a wireless connection between a mobile DAU and the stationary central system unit. The Bluetooth module provides wireless connection between two transceivers in a range of about 10m ie the vicinity of a room.
d. Atmel 89C52 Microcontroller
The Atmel 89C52 is the microcontroller which forms the core of the DAU. It enhances serial data transmission, bidirectionally. The 89C52 is chosen since it is has well established industrial standards and it provide necessary functionality. It has a very high speed serial port which makes the serial data transmission smooth. All the DAU software is written in the 8051 assembler code and the assembler used here is the AS-31. This ensures highest program efficiency and lowest resource utilization.

e. PCM Codec
Since the Bluetooth module supports the synchronous voice data transmission, the Blue Eyes hardware uses PCM Codec to transmit operator's voice and central system sound feedback. The codec employed reduces the microcontroller's task and lessens the amount of data being send over the UART. The PCM Codec performs voice data compression, which results in smaller bandwidth utilization and better sound quality. The codec used here is Motorola MC 145483 linear 13 bit 3.3V codec which is voltage level compatible with Bluetooth module.
f. Beeper, LED, LCD and Keyboard
The central system unit will sense the user defined alarm conditions from the input given to it by the DAU. The beeper will produce alarm sound when the exceptions are detected. It is used to inform the user or his colleagues about the exceptions detected.
A simple keyboard is provided to react to the incoming events, ie for example to silence the alarm sound. This keyboard is also used to enter the personal PIN code, while performing the authorization procedure.
The alphanumeric LCD display gives more information about the alarm conditions. It helps the user to enter his personal PIN number accurately.
In the authorization process ,if the user is not entering his ID card, the system will undergo a self test and the LED indicators associated with the DAU will shows the output of the self test. It also shows the current power level and the state of wireless connection.
An assembled DAU which is developed in the Poznan University is shown below.


DAU stores the received data from the sensor in an internal buffer, after the whole frame is completed it encapsulates the data in an ACL frame and send it over the Bluetooth link. The fetching phase takes up approximate 192us ( 8us x 24 frames) and the sending phase takes at 115200 bps approximately.
In the DAU there are two independent data sources- the Jazz sensor and the Bluetooth host controller. Since both are handled using the interrupt system, it is necessary to decide which of the source should have higher priority. Giving the sensor data the highest priority may results in losing some of the data send by the Bluetooth module, as the transmission of the sensor data takes twice as much time as receiving one byte data from UART. Missing a single byte data send from the Bluetooth causes the lose of control over the transmission. On other hand, giving the Bluetooth the highest priority will make the DAU stop receiving the sensor data until the host controller finishes its transmission.In this case the interrupted sensor frame shall be discarded. We do not consider the data being send from the DAU to the Bluetooth as it does not affect the operation. Since the missing 1 byte of Bluetooth communication affects the functioning of DAU much more than losing one single sensor data frame, the high priority is obviously given to the Bluetooth link.
4.3 Central System Unit
The central system unit constitute the second peer of wireless connection. This box contains the Bluetooth transceiver and PCM codec for voice data transmission. The main functions of the CSU includes maintaining Bluetooth connection, buffering incoming sensor data, performing online data analysis, recording the results for further reference and providing visualization.
The main parts which constitute the CSU are connection manager, data logger module, data analysis module, alarm dispatcher module and visualization module. The connection manager forms the front end of the CSU which receives the incoming data. The data logger module buffers the incoming data and the processed information. The data analysis module processes the output of the sensor and identifies the emotion. The incoming data is loaded into the alarm dispatcher module together with the data logger. This module checks the alarm conditions and produce suitable output. The visualization module provides a convenient way for the supervisor to access the database.
CSU - intermodule communication
C onnection Manager

Ope ra tor manager (OM)
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Processed data
Recorded (off-line) data

The individual units which constitute the CSU are explained below.
a. Connection Manager
Connection Manager's main task is to perform low-level Bluetooth communication using host Controller Interface commands. It is designed to cooperate with all available Bluetooth devices. Additionally, Connection Manager authorizes operators, manages their sessions, demultiplexes and buffers raw physiological data.
Bluetooth Connection Manager is responsible for establishing and maintaining connections using all available Bluetooth devices. It periodically inquires new devices in an operating range and checks whether they are registered in the system database. Only with those devices, the Connection Manager will communicate.
After establishing a connection an authentication procedure occurs. The authentication process is performed using system PIN code fetched from the database. Once the connection has been authenticated the mobile unit sends a data frame containing the operator's identifier. Finally, the Connection Manager adds a SCO link ( voice connection) and runs a new dedicated Operator Manager, which will manage the new operator's session..
b. Operator Manager
The data of each supervised operator is buffered separately in the dedicated Operator Manager. At the startup it communicates with the Operator Data Manager in order to get more detailed personal data. The most important Operator Manager's task is to buffer the incoming raw data and to split it into separate data streams related to each of the measured parameters. The raw data is sent to a Logger Module, the split data streams are available for the other system modules through producer-consumer queues. Furthermore, the Operator Manager provides an interface for sending alert messages to the related operator.
Operator Data Manager provides an interface to the operator database enabling the other modules to read or write personal data and system access information.
c. Data Logger Module
The module provides support for storing the monitored data in order to enable the supervisor to reconstruct and analyze the course of the operator's duty. The module registers as a consumer of the data to be stored in the database. Each working operator's data is recorded by a separate instance of the Data Logger. Apart from the raw or processed physiological data, alerts and operator's voice are stored. The raw data is supplied by the related Operator Manager module, whereas the Data Analysis module delivers the processed data. The voice data is delivered by a Voice Data Acquisition module. The module registers as an operator's voice data consumer and optionally processes the sound to be stored (i.e. reduces noise or removes the fragments when the operator does not speak). The Logger's task is to add appropriate time stamps to enable the system to reconstruct the voice.
d. Data Analysis Module
The module performs the analysis of the raw sensor data in order to obtain information about the operator's physiological condition. The separately running Data Analysis Module supervises each of the working operators. The module consists of a number of smaller analyzers extracting different types of information. Each of the analyzers registers at the appropriate Operator Manager or another analyzer as a data consumer and, acting as a producer, provides the results of the analysis
e. Alarm Dispatcher Module
Alarm Dispatcher Module is a very important part of the Data Analysis module. It registers for the results of the data analysis, checks them with regard to the user-defined alarm conditions and launches appropriate actions when needed. The module is a producer of the alarm messages, so that they are accessible in the logger and visualization modules.
f. Visualization Module
The module provides user interface for the supervisors. It enables them to watch each of the working operator's physiological condition along with a preview of selected video source and his related sound stream. All the incoming alarm messages are instantly signaled to the supervisor. Moreover, the visualization module can be set in the off-line mode, where all the data is fetched from the database. Watching all the recorded physiological parameters, alarms, video and audio data the supervisor is able to reconstruct the course of the selected operator's duty.
5. APPLICATIONS OF BLUE EYES
BLUE EYES enables the computer to adapt a working style that fits users personality to increase his productivity
The technology can also be incorporated into automobiles and the interactive entertainment such as toys etc. An alarm can be devised in the steering of a vehicle to warn the driver, if his stress level goes beyond the critical level. Also, Blue Eyes is going to find its way in the routines of human beings with application such as TV, washing machine etc.
Also if we take case of a company, the supervisor can be given the power to access the database containing his employees profile, which is linked with Blue Eyes technology. Thus he can check the emotional state of any of his worker at any time at his own will. Thereby he can evaluate the performance of his employees. For example, the boss can monitor the saccadic activity of a worker at his night duty time, and if the saccadic activity is smaller, the boss can infer that the worker is sleeping. He can trigger an alarm at this condition or can inform others to wake him up.
6. FUTURE SCOPE
We find it possible still to improve our project. The use of a miniature CMOS camera integrated into the eye movement sensor will enable the system to calculate the point of gaze and observe what the operator is actually looking at.
Introducing voice recognition algorithm will facilitate the communication between the operator and the central system and simplify authorization process.
Future applications of this technology is limitless -from designing cars to controlling your household devices.
7. CONCLUSION
The BLUE EYES is the most modern technology, which dealt with giving the computers emotional intelligence. This innovation can make our life so simple that anybody can operate any household devices, sophisticated machines, manufacturing machines in industries which have complicated operating procedures etc without much conscious effort. By the implementation of this technology we can have devices which will do our tasks when we speak to them. We will work with our personal computer which can hear us, speak to us and even scream aloud. This amazing technology will simplify our life by providing more delicate means to operate our devices. Thus the BLUE EYES will find its way to our day-to-day life and will become an integral part of it.



2. 3. 4.


http://www.wikipedia.org ,ets
ROK101008 Bluetooth Module, Erickson Microelectronics

CONTENTS
1. INTRODUCTION .1
2. BIOMETRICS 2
3. MAIN BIOMETRIC TECHNIQUES 3
3.1 FACIAL EXPRESSION DETECTION 3
3.2 SPEECH RECOGNITION 4
3.3 EMOTION MOUSE 4
3.4 EYE GAZE TRACKING 5
3.5 JAZZ MULTISENSOR 5
4. BLUE EYES HARDWARE 8
4.1 SYSTEM OVERVIEW 8
4.2 DATA ACQUISITION UNIT 10
4.3 CENTRAL SYSTEM UNIT 14
5. APPLICATION 18
6. FUTURE SCOPE 19
7. CONCLUSION 20
8. REFERENCE 21
19-02-2010, 11:16 AM
Post: #5
RE: Blue eyes (Download Full Report And Abstract)

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Introduction
Developed by IBM at Almaden Research centre.
Aims at giving computers highly developed abilities.
Enables computers and humans to work together more as partners.
Employs video camers.

Basic Principle

Human physiological traits are reliable resources for identification of emotions.
Measures GSR, heart rate, skin temperature.
Measures six basic emotions.
Data subjected to two analysis
1. Multidimensional scaling (MDS)
2. Discriminant function analysis.
Physiological measurements were correlated to emotions.

.
Recent developments

MAGIC Pointing
Eye tracker
Emotion Mouse
MAGIC POINTING

Pointing and selection were aided by gaze tracking.
Use gaze to redefine the position of cursor.
Two MAGIC pointing techniques are,
Liberal
Conservative

Advantages of magic pointing

Accurate
High speed
Reduce manual stress and fatigue.
Ease of use

Disadvantages

Eye gaze is not precise enough to operate
Movements of eyes are voluntarily controlled while at other times it is driven by external events.
Eye tracker
More compact and reliable
Illumination from an off-axis and on-axis source generates a dark pupil image and bright pupil image.
Working.

Advantages of eye tracker

Very Robust.
Able to detect pupils from wild field of view.
Range up to 5m from camera.
Can be used for even people with glasses.
Fast and low cost pupil detection technique.
EMOTION MOUSE

Emotions are important to the computing community.
A non-invasive way to obtain information about a user is through touch.
The computer determines the user's emotional state by a simple touch on the mouse.
Sensing of physiological attributes, related to emotions.

SIMPLE USER INTEREST TRACKER

SUITOR-The Simple User Interest Tracker.
Finds out the areas of interest of the user.
The attentive system

FUTURE DEVELOPMENTS

Security purposes.
ordinary household devices .
Eg : Working of television , refrigerators & ovens when we look at them.

REFERENCE

Affective Computing - Picard. R.
Advantages of Eye Gaze Interaction- Silbert.L.
Bluetooth Module, Ericsson Microelectronics,
BLUE EYES system overview, Datasheet, Poznan University.
http://www.eetimes.com
http://www.ibm.com
16-03-2010, 07:09 PM
Post: #6
RE: Blue eyes (Download Full Report And Abstract)

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1. INTRODUCTION
BlueEyes system provides technical means for monitoring and recording the operatorâ„¢s basic physiological parameters. The most important parameter is saccadic activity1, which enables the system to monitor the status of the operatorâ„¢s visual attention along with head acceleration, which accompanies large displacement of the visual axis (saccades larger than 15 degrees). Complex industrial environment can create a danger of exposing the operator to toxic substances, which can affect his cardiac, circulatory and pulmonary systems. Thus, on the grounds of plethysmographic signal taken from the forehead skin surface, the system computes heart beat rate and blood oxygenation.
The BlueEyes system checks above parameters against abnormal (e.g. a low level of blood oxygenation or a high pulse rate) or undesirable (e.g. a longer period of lowered visual attention) values and triggers user-defined alarms when necessary. Quite often in an emergency situation operator speak to themselves expressing their surprise or stating verbally the problem. Therefore, the operatorâ„¢s voice, physiological parameters and an overall view of the operating room are recorded. This helps to reconstruct the course of operatorsâ„¢ work and provides data for long-term analysis.
BlueEyes consists of a mobile measuring device and a central analytical system. The mobile device is integrated with Bluetooth module providing wireless interface between sensors worn by the operator and the central unit. ID cards assigned to each of the operators and adequate user profiles on the central unit side provide necessary data personalization so different people can use a single mobile device (called hereafter DAU “ Data Acquisition Unit). The overall system diagram is shown in Figure 1. The tasks of the mobile Data Acquisition Unit are to maintain Bluetooth connections, to get information from the sensor and sending it over the wireless connection,
1 Saccades are rapid eye jumps to new locations within a visual environment assigned predominantly by the conscious attention process.
to deliver the alarm messages sent from the Central System Unit to the operator and handle personalized ID cards. Central System Unit maintains the other side of the Bluetooth connection, buffers incoming sensor data, performs on-line data analysis, records the conclusions for further exploration and provides visualization interface.
The task of the mobile Data Acquisition Unit are to maintain Bluetooth connection, to get information from the sensor and sending it over the wireless connection ,to deliver the alarm messages sent from the Central System Unit to the operator and handle personalized ID cards. Central System Unit maintains the other side of the Bluetooth connection, buffers incoming sensor data, performs on-line data analysis, records the conclusion for further exploration and provides visualization interface.
1.1. PERFORMANCE REQUIREMENTS
The portable nature of the mobile unit results in a number of performance requirements. As the device is intended to run on batteries, low power consumption is the most important constraint. Moreover, it is necessary to assure proper timing while receiving and transmitting sensor signals. To make the operation comfortable the device should be lightweight and electrically safe. Finally the use of standard and inexpensive ICâ„¢s will keep the price of the device at relatively low level.
The priority of the central unit is to provide real-time buffering and incoming sensor signals and semi-real-time processing of the data, which requires speed-optimizes filtering and reasoning algorithms. Moreover, the design should assure the possibility of distributing the processing among two or more central unit nodes (e.g. to offload the database system related tasks to a dedicated server).
1.2. DESIGN METHODOLOGIES

In creating the BlueEyes system a waterfall software development model was used since it is suitable for unrepeatable and explorative projects. During the course of the development UML standard notations were used. They facilitate communication between team members, all the ideas are clearly expressed by means of various diagrams, which is a sound base for further development.
The results of the functional design phase were documented on use case diagrams. During the low-level design stage the whole systems was divided into five main modules. Each of them has an independent, well-defined functional interface providing precise description of the services offered to the other modules. All the interfaces are documented on UML class, interaction and state diagrams. At this point each of the modules can be assigned to a team member, implemented and tested in parallel. The last stage of the project is the integrated system testing.
1.3. INNOVATIVE IDEAS

The unique feature of our system relies on the possibility of monitoring the operatorâ„¢s higher brain functions involved in the acquisition of the information from the visual environment. The wireless link between the sensors worn by the operator and the supervising system offers new approach to system overall reliability and safety. This gives a possibility to design a supervising module whose role is to assure the proper quality of the system performance. The new possibilities can cover such areas as industry, transportation (by air, by road and by sea), military command centers or operating theaters (anesthesiologists).
2. IMPLEMANTATION OF BLUE EYES TECHNOLOGY
2.1. FUNCTIONAL DESIGN
During the functional design phase we used UML standard use case notation, which shows the functions the system offers to particular users. BlueEyes has three groups of users: operators, supervisors and system administrators. Operator is a person whose physiological parameters are supervised. The operator wears the DAU. The only functions offered to that user are authorization in the system and receiving alarm alerts. Such limited functionality assures the device does not disturb the work of the operator (Fig. 2).

Figure 2: Mobile Device User
Authorization “ the function is used when the operator™s duty starts. After inserting his personal ID card into the mobile device and entering proper PIN code the device will start listening for incoming Bluetooth connections. Once the connection has been established and authorization process has succeeded (the PIN code is correct) central system starts monitoring the operator™s physiological parameters. The authorization process shall be repeated after reinserting the ID card. It is not, however, required on reestablishing Bluetooth connection.
Receiving alerts “ the function supplies the operator with the information about the most important alerts regarding his or his co-workers™ condition and mobile device state (e.g. connection lost, battery low). Alarms are signaled by using a beeper, earphone providing central system sound feedback and a small alphanumeric LCD display, which shows more detailed information.
Supervisor is a person responsible for analyzing operatorsâ„¢ condition and performance. The supervisor receives tools for inspecting present values of the parameters (On-line browsing) as well as browsing the results of long-term analysis (Off-line browsing).
During the on-line browsing it is possible to watch a list of currently working operators and the status of their mobile devices. Selecting one of the operators enables the supervisor to check the operatorâ„¢s current physiological condition (e.g. a pie chart showing active brain involvement) and a history of alarms regarding the operator. All new incoming alerts are displayed immediately so that the supervisor is able to react fast. However, the presence of the human supervisor is not necessary since the system is equipped with reasoning algorithms and can trigger user-defined actions (e.g. to inform the operatorâ„¢s co-workers).
During off-line browsing it is possible to reconstruct the course of the operatorâ„¢s duty with all the physiological parameters, audio and video data. A comprehensive data analysis can be performed enabling the supervisor to draw conclusions on operatorâ„¢s overall performance and competency (e.g. for working night shifts).
System administrator is a user that maintains the system. The administrator delivers tools for adding new operators to the database, defining alarm conditions,
configuring logging tools and creating new analyzer modules.
While registering new operators the administrator enters appropriate data (and a photo if available) to the system database and programs his personal ID card.
Defining alarm conditions “ the function enables setting up user-defined alarm conditions by writing condition-action rules (e.g. low saccadic activity during a longer period of time inform operator™s co-workers, wake him up using the beeper or playing appropriate sound and log the event in the database).
Designing new analyzer modules-based on earlier recorded data the administrator can create new analyzer module that can recognize other behaviors than those which are built-in the system. The new modules are created using decision tree induction algorithm. The administrator names the new behavior to be recognized and points the data associated with it. The results received from the new modules can be used in alarm conditions.
Monitoring setup enables the administrator to choose the parameters to monitor as well as the algorithms of the desired accuracy to compute parameter values.
Logger setup provides tools for selecting the parameters to be recorded. For audio data sampling frequency can be chosen. As regards the video signal, a delay between storing consecutive frames can be set (e.g. one picture in every two seconds).
Database maintenance “ here the administrator can remove old or uninteresting data from the database. The uninteresting data is suggested by the built-in reasoning system.
2.2. DATA ACQUISITION UNIT (DAU)

This section deals with the hardware part of the BlueEyes system with regard to the physiological data sensor, the DAU hardware components and the microcontroller software.
2.2.1. PHYSIOLOGICAL DATA SENSOR
To provide the Data Acquisition Unit with necessary physiological data an off-shelf eye movement sensor “ Jazz Multisensor is used. It supplies raw digital data regarding eye position, the level of blood oxygenation, acceleration along horizontal and vertical axes and ambient light intensity. Eye movement is measured using direct infrared oculographic transducers. (The eye movement is sampled at 1 kHz, the other parameters at 250 Hz. The sensor sends approximately 5.2 kB of data per second.)
2.2.2. HARDWARE SPECIFICATION
Microcontrollers (e.g. Atmel 8952 microcontroller)can be used as the core of the Data Acquisition Unit since it is a well-established industrial standard and provides necessary functionalities(i.e. high speed serial port)at a low price.
The Bluetooth module supports synchronous voice data transmission .The codec reduces the microcontrollerâ„¢s tasks and lessens the amount of data being sent over the UART. The Bluetooth module performs voice data compression, which results in smaller bandwidth utilization and better sound quality.
.
Communication between the Bluetooth module and the microcontroller is carried on using standard UART interface. The speed of the UART is set to 115200 bps in order to assure that the entire sensor data is delivered in time to the central system.
The alphanumeric LCD display gives more information of incoming events and helps the operator enter PIN code. The LED indicators shows the result of built-in-self-test, power level and the state of wireless connection.
The simple keyboard is used to react to incoming events and to enter PIN code while performing authorization procedure. The ID card interface helps connect the operatorâ„¢s personal identification card to the DAU. After inserting the card authorization procedure starts. The operatorâ„¢s unique identifier enables the supervising system to distinguish different operators.
2.2.3. MICROCONTROLLER SOFTWARE SPECIFICATION
DAU software is written in assembler code, which assures the highest program efficiency and the lowest resource utilization. The DAU communicates with the Bluetooth module using Host Controller Interface (HCI) commands

In the No ID card state a self-test is performed to check if the device is working correctly. After the self-test passes the sensor and Bluetooth module are reset and some initialization commands are issued(i.e. HCI_Reset, HCI_Ericsson_Set_UART_Baud_Rate etc.). Once the initialization has been successfully completed the device starts to check periodically for ID card presence by attempting to perform an I2C start condition. When the attempt succeeds and the operatorâ„¢s identifier is read correctly the device enters User authorization state.
In the User authorization state the operator is prompted to enter his secret PIN code. If the code matches the code read from the inserted ID card the device proceeds waiting for incoming Bluetooth connections.
On entering Waiting for connection state the DAU puts the Bluetooth module in Inquiry and Page Scan mode. After the first connection request appears, the DAU accepts it and enters Connection authentication state.
In the Connection authentication state the DAU issues Authentication Requested HCI command. On Link Controllerâ„¢s Link_Key_Request the DAU sends Link_Key_Negative_Reply in order to force the Bluetooth module to generate the link key based on the supplied system access PIN code. After a successful authentication the DAU enters the Data Processing state, otherwise it terminates the connection and enters the Waiting for connection state.
The main DAU operation takes place in the Data Processing state. In the state five main kinds of events are handled. Since the sensor data has to be delivered on time to the central system, data fetching is performed in high-priority interrupt handler procedure. Every 4ms the Jazz sensor raises the interrupt signaling the data is ready for reading. The following data frame is used:
Figure 6: Jazz Sensor frame format
The preamble is used to synchronize the beginning of the frame, EyeX represents the horizontal position of the eye, EyeY “ vertical, AccX and AccY “ the acceleration vectors along X and Y axes, PulsoOxy, Batt and Light “ blood oxygenation, voltage level and light intensity respectively. The figure below shows the sensor communication timing.
Figure 7: Jazz Sensor data fetching waveform
The received data is stored in an internal buffer; after the whole frame is completed the DAU encapsulates the data in an ACL frame and sends it over the Bluetooth link. (The fetching phase takes up approx. 192s (24 frames x 8s) and the sending phase takes at 115200 bps approx. 2,8 ms, so the timing fits well in the 4ms window.) In every state removing the ID card causes the device to enter the No ID card state, terminating all the established connections.
The second groups of events handled in the Data Processing state are system messages and alerts. They are sent from the central system using the Bluetooth link. Since the communication also uses microcontrollers interrupt system the events are delivered instantly.
The remaining time of the microcontroller is utilized performing LCD display, checking the state of the buttons, ID card presence and battery voltage level. Depending on which button is pressed appropriate actions are launched. In every state removing the ID card causes the device to enter the No ID card state terminating all the established connections.
In the DAU there are two independent data sources-Jazz sensor and Bluetooth Host Controller. Since they are both handled using the interrupt system it is necessary to decide which of the sources should have higher priority. Giving the sensor data the highest priority may result in losing some of the data sent by the Bluetooth module,as the transmission of the sensor data takes twice as much time as receiving one byte from UART. Missing one single byte sent from the Bluetooth causes the loss of control over the transmission. On the other hand, giving the Bluetooth the highest priority will make the DAU stop receiving the sensor data until the Host Controller finishes its transmission. Central system alerts are the only signals that can appear during sensor data fetching after all the unimportant Bluetooth events have been masked out. The best solution would be to make the central unit synchronize the alerts to be sent with the Bluetooth data reception. As the delivered operating system is not a real-time system, the full synchronization is not possible.
As the Bluetooth module communicates asynchronously with the microcontroller there was a need of implementing a cyclic serial port buffer, featuring UART CTS/RTS flow control and a producer-consumer synchronization mechanism.
2.3. CENTARL SYSTEM UNIT (CSU)
CSU software is located on the delivered Computer/System; in case of larger resource demands the processing can be distributed among a number of nodes. In this section we describe the four main CSU modules (see Fig. 1): Connection Manager, Data Analysis, Data Logger and Visualization. The modules exchange data using specially designed single-producer multi consumer buffered thread-safe queues. Any number of consumer modules can register to receive the data supplied by a producer. Every single consumer can register at any number of producers, receiving therefore different types of data. Naturally, every consumer may be a producer for other consumers. This approach enables high system scalability “ new data processing modules (i.e. filters, data analyzers and loggers) can be easily added by simply registering as a consumer.
2.3.1. CONNECTION MANAGER
Connection Managerâ„¢s main task is to perform low-level Bluetooth communication using Host.
Figure 8: Connection Manager Components
Controller Interface commands. It is designed to cooperate with all available Bluetooth devices in order to support roaming. Additionally, Connection Manager authorizes operators, manages their sessions, demultiplexes and buffers raw physiological data. Figure 11 shows Connection Manager Architecture.
Transport Layer Manager hides the details regarding actual Bluetooth physical transport interface (which can be either RS232 or UART or USB standard) and provides uniform HCI command interface.
Bluetooth Connection Manager is responsible for establishing and maintaining connections using all available Bluetooth devices. It periodically inquires new devices in an operating range and checks whether they are registered in the system database. Only with those devices the Connection Manager will communicate. After establishing a connection an authentication procedure occurs. The authentication process is performed using system PIN code fetched from the database. Once the connection has been authenticated the mobile unit sends a data frame containing the operatorâ„¢s identifier. Finally, the Connection Manager adds a SCO link (voice connection) and runs a new dedicated Operator Manager, which will manage the new operatorâ„¢s session. Additionally, the Connection Manager maps the operatorâ„¢s identifiers into the Bluetooth connections, so that when the operators roam around the covered area a connection with an appropriate Bluetooth device is established and the data stream is redirected accordingly.
The data of each supervised operator is buffered separately in the dedicated Operator Manager. At the startup it communicates with the Operator Data Manager in order to get more detailed personal data. The most important Operator Managerâ„¢s task is to buffer the incoming raw data and to split it into separate data streams related to each of the measured parameters. The raw data is sent to a Logger Module, the split data streams are available for the other system modules through producer-consumer queues. Furthermore, the Operator Manager provides an interface for sending alert messages to the related operator.
Operator Data Manager provides an interface to the operator database enabling the other modules to read or write personal data and system access information.
2.3.2. DATA ANALYSIS MODULE
The module performs the analysis of the raw sensor data in order to obtain information about the operatorâ„¢s physiological condition. The separately running Data Analysis Module supervises each of the working operators. The module consists of a number of smaller analyzers extracting different types of information. Each of the analyzers registers at the appropriate Operator Manager or another analyzer as a data consumer and, acting as a producer, provides the results of the analysis. An analyzer can be either a simple signal filter (e.g. Finite Input Response (FIR) filter) or a generic data extractor (e.g. signal variance, saccade detector) or a custom detector module. As it is not able to predict all the supervisorsâ„¢ needs, the custom modules are created by applying a supervised machine learning algorithm to a set of earlier recorded examples containing the characteristic features to be recognized. In the prototype we used an improved C4.5 decision tree induction algorithm. The computed features can be e.g. the operatorâ„¢s position (standing, walking and lying) or whether his eyes are closed or opened.
As built-in analyzer modules we implemented a saccade detector, visual attention level, blood oxygenation and pulse rate analyzers.
The saccade detector registers as an eye movement and accelerometer signal variance data consumer and uses the data to signal saccade occurrence. Since saccades are the fastest eye movements the algorithm calculates eye movement velocity and checks physiological Constraints. The algorithm has two main steps:
User adjustment step. The phase takes up 5 s. After buffering approx. 5 s of the signal differentiate it using three point central difference algorithm, which will give eye velocity time series. Sort the velocities by absolute value and calculate upper 15% of the border velocity along both X “ v0x and Y “ v0y axes . As a result v0x and v0y are cut-off velocities.
On-line analyzer flow. Continuously calculate eye movement velocity using three point central difference algorithms. If the velocity excess pre calculated v0 (both axes are considered separately) there is a possibility of saccade occurrence. Check the following conditions (if any of them is satisfied do not detect a saccade):
¢the last saccade detection was less than 130 ms ago (physiological constraint “ the saccades will not occur more frequently)
¢the movement is nonlinear (physiological constraint)
¢compare the signal with accelerometer (rapid head movement may force eye activity of comparable speed)
¢if the accelerometer signal is enormously uneven consider ignoring the signal due to possible sensor device movements.
If none of the above conditions is satisfied “ signal the saccade occurrence.
The visual attention level analyzer uses as input the results produced by the saccade detector. Low saccadic activity (large delays between subsequent saccades) suggests lowered visual attention level (e.g. caused by thoughtfulness). Thus, we propose a simple algorithm that calculates the visual attention level (Lva): Lva = 100/ts10, where ts10 denotes the time (in seconds) occupied by the last ten saccades. Scientific research has proven [1] that during normal visual information intake the time between consecutive saccades should vary from 180 up to 350 ms. this gives Lva at 28 up to 58 units. The values of Lva lower than 25 for a longer period of time should cause a warning condition. The following figure shows the situation where the visual attention lowers for a few seconds.

Figure 9: Saccade occurrence and Visual attention level
The Pulse rate analyzer registers for the oxyhemoglobin and deoxyhemoglobin level data streams. Since both signals contain a strong sinusoidal component related to heartbeat, the pulse rate can be calculated measuring the time delay between subsequent extremes of one of the signals. We decided not to process only one of the data streams “ the algorithm is designed to choose dynamically one of them on the grounds of the signal level. Unfortunately, the both signals are noised so they must be filtered before further processing. We considered a number of different algorithms and decided to implement average value based smoothing. More detailed discussion is presented in section 3.3.5 Tradeoffs and Optimization. The algorithm consists in calculating an average signal value in a window of 100 samples. In every step the window is advanced 5 samples in order to reduce CPU load. This approach lowers the sampling rate from 250 Hz down to 50 Hz. However, since the Visual heartbeat frequency is at most 4 Hz the Nyquist condition remains satisfied. The figures show the signal before (Fig. 10a) and after filtering (Fig 10b).
Figure: 10(a) Figure: 10(b)
After filtering the signal the pulse calculation algorithm is applied. The algorithm chooses the point to be the next maximum if it satisfies three conditions: points on the left and on the right have lower values, the previous extreme was a minimum, and the time between the maximums is not too short (physiological constraint). The new pulse value is calculated based on the distance between the new and the previous maximum detected. The algorithm gets the last 5 calculated pulse values and discards 2 extreme values to average the rest. Finally, it does the same with the minimums of the signal to obtain the second pulse rate value, which gives the final result after averaging.
Additionally, we implemented a simple module that calculates average blood oxygenation level. Despite its simplicity the parameter is an important measure of the operatorâ„¢s physiological condition.
The other signal features that are not recognized by the built-in analyzers can be extracted using custom modules created by Decision Tree Induction module. The custom module processes the generated decision tree, registers for needed data streams and produces the desired output signal.
Decision Tree Induction module generates the decision trees, which are binary trees with an attribute test in each node. The decision tree input data is an object described by means of a set of attribute-value pairs. The algorithm is not able to process time series directly. The attributes therefore are average signal value, signal variance and the strongest sinusoidal components. As an output the decision tree returns the category the object belongs to. In the Decision Tree Induction module we mainly use C 4.5 algorithm [2], but also propose our own modifications. The algorithm is a supervised learning from examples i.e. it considers both attributes that describe the case and a correct answer. The main idea is to use a divide-and-conquer approach to split the initial set of examples into subsets using a simple rule (i-th attribute less than a value). Each division is based on entropy calculation “ the distribution with the lowest entropy is chosen. Additionally, we propose many modifications concerning some steps of the algorithm and further exploration of the system.
For each case to be classified C 4.5 traverses the tree until reaching the leaf where appropriate category id is stored. To increase the hit ratio our system uses more advanced procedure. For single analysis we develop a group of k trees (where k is a parameter), which we call a decision forest. Initial example set S is divided randomly into k+1 subsets S0 ... Sk. S0 is left to test the whole decision forest. Each tree is induced using various modifications of the algorithm to provide results™ independence. Each i-th tree is taught using S\S0\Si set (S without S0 and Si sets) and tested with Si that estimates a single tree error ratio. Furthermore we extract all wrongly classified examples and calculate correlation matrix between each pair of the trees. In an exploring phase we use unequal voting rule “ each tree has a vote of strength of its reliability. Additionally, if two trees give the same answer their vote is weakened by the correlation ratio.
Alarm Dispatcher Module is a very important part of the Data Analysis module. It registers for the results of the data analysis, checks them with regard to the user-defined alarm conditions and launches appropriate actions when needed. The module is a producer of the alarm messages, so that they are accessible in the logger and visualization modules.
2.3.3. DATA LOGGER MODULE
The module provides support for storing the monitored data in order to enable the supervisor to reconstruct and analyze the course of the operatorâ„¢s duty. The module registers as a consumer of the data to be stored in the database. Each working operatorâ„¢s data is recorded by a separate instance of the Data Logger. Apart from the raw or processed physiological data, alerts and operatorâ„¢s voice are stored. The raw data is supplied by the related Operator Manager module, whereas the Data Analysis module delivers the processed data. The voice data is delivered by a Voice Data Acquisition module. The module registers as an operatorâ„¢s voice data consumer and optionally processes the sound to be stored (i.e. reduces noise or removes the fragments when the operator does not speak). The Loggerâ„¢s task is to add appropriate time stamps to enable the system to reconstruct the voice.
Additionally, there is a dedicated video data logger, which records the data supplied by the Video Data Acquisition module (in the prototype we use JPEG compression). The module is designed to handle one or more cameras using Video for Windows standard. The Data Logger is able to use any ODBC-compliant database system. In the prototype we used MS SQL Server, which is a part of the Project Kit.
2.3.4. VISUALIZATION MODULE
The module provides user interface for the supervisors. It enables them to watch each of the working operatorâ„¢s physiological condition along with a preview of selected video source and his related sound stream. All the incoming alarm messages are instantly signaled to the supervisor. Moreover, the visualization module can be set in the off-line mode, where all the data is fetched from the database. Watching all the recorded physiological parameters, alarms, video and audio data the supervisor is able to reconstruct the course of the selected operatorâ„¢s duty.
2.4. TOOLS USED TO DEVELOP BLUEEYES
In creating the hardware part of the DAU a development board was built, which enabled to mount, connect and test various peripheral devices cooperating with the microcontroller. During the implementation of the DAU there was a need for a piece of software to establish and test Bluetooth connections. Hence created a tool called Blue Dentist. The tool provides support for controlling the currently connected Bluetooth device. Its functions are: Local device management (resetting, reading local BD_ADDR, putting in Inquiry/Page and Inquiry/Page scan modes, reading the list of locally supported features and setting UART speed) and connection management (receiving and displaying Inquiry scan results, establishing ACL links, adding SCO connections, performing link authorization procedure, sending test data packets and disconnecting).
Fig: Blue Dentist
To test the possibilities and performance of the remaining parts of the Project Kit (computer, camera and database software) Blue Capture (Fig. 12) was created. The tool supports capturing video data from various sources (USB web-cam, industrial camera) and storing the data in the MS SQL Server database. Additionally, the application performs sound recording. After filtering and removing insignificant fragments (i.e. silence) the audio data is stored in the database. Finally, the program plays the recorded audiovisual stream. They used the software to measure database system performance and to optimize some of the SQL queries (e.g. we replaced correlated SQL queries with cursor operations).
Figure 12: BlueCapture
Also a simple tool for recording Jazz Multisensory measurements was introduced. The program reads the data using a parallel port and writes it to a file. To program the operatorâ„¢s personal ID card we use a standard parallel port, as the EEPROMs and the port are both TTL-compliant. A simple dialog-based application helps to accomplish the task.
3. SUMMARY
The BlueEyes system is developed because of the need for a real-time monitoring system for a human operator. The approach is innovative since it helps supervise the operator not the process, as it is in presently available solutions. We hope the system in its commercial release will help avoid potential threats resulting from human errors, such as weariness, oversight, tiredness or temporal indisposition. However, the prototype developed is a good estimation of the possibilities of the final product. The use of a miniature CMOS camera integrated into the eye movement sensor will enable the system to calculate the point of gaze and observe what the operator is actually looking at. Introducing voice recognition algorithm will facilitate the communication between the operator and the central system and simplify authorization process.
Despite considering in the report only the operators working in control rooms, our solution may well be applied to everyday life situations. Assuming the operator is a driver and the supervised process is car driving it is possible to build a simpler embedded on-line system, which will only monitor conscious brain involvement and warn when necessary. As in this case the logging module is redundant, and the Bluetooth technology is becoming more and more popular, the commercial implementation of such a system would be relatively inexpensive.
The final thing is to explain the name of our system. BlueEyes emphasizes the foundations of the project “ Bluetooth technology and the movements of the eyes. Bluetooth provides reliable wireless communication whereas the eye movements enable us to obtain a lot of interesting and important information.

4. REFERENCE
1. Carpenter R. H. S., Movements of the eyes, 2nd edition, Pion Limited, 1988, London.
2. Bluetooth specification, version 1.0B, Bluetooth SIG, 1999.
3. ROK 101 007 Bluetooth Module ,Ericsson Microelectronics,2000.
4. AT89C52 8-bit Microcontroller Datasheet, Atmel.
5. Intel Signal Processing Library “Reference Manual.
BLUE EYES
TECHNOLOGY
SUBMITTED BY:
GEETHU GOPINATH
S7CS
ROLL NO: 7341
ABSTRACT
Human error is still one of the most frequent causes of catastrophes and ecological disasters. The main reason is that the monitoring systems concern only the state of the processes whereas human contribution to the overall performance of the system is left unsupervised. Since the control instruments are automated to a large extent, a human “ operator becomes a passive observer of the supervised system, which results in weariness and vigilance drop. This, he may not notice important changes of indications causing financial or ecological consequences and a threat to human life.
It therefore is crucial to assure that the operatorâ„¢s conscious brain is involved in an active system supervising over the whole work time period. It is possible to measure indirectly the level of the operatorâ„¢s conscious brain involvement using eye motility analysis. Although there are capable sensors available on the market, a complex solution enabling transformation, analysis and reasoning based on measured signals still does not exist. In large control rooms, wiring the operator to the central system is a serious limitation of his mobility and disables his operation. Utilization of wireless technology becomes essential.
Blue Eyes is intended to be the complex solution for monitoring and recording the operatorâ„¢s conscious brain involvement as well as his Physiological condition. This required designing a Personal Area Network linking all the Operators and the supervising system. As the operator using his sight and hearing senses the state of the controlled system, the supervising system will look after his physiological condition.
CONTENTS
1. INTRODUCTION 1
1.1. PERFORMANCE REQUIREMENTS 3
1.2. DESIGN METHODOLOGIES 3
1.3. INNOVATIVE IDEAS 4
2. IMPLEMANTATION OF BLUEEYES
TECHNOLOGY 5
2.1. FUNCTIONAL DESIGN 5
2.2. DATA ACQUISITION UNIT (DAU) 9
2.2.1. PHYSIOLOGICAL DATA SENSOR 9
2.2.2. HARDWARE SPECIFICATION 10
2.2.3. MICROCONTROLLER SOFTWARE SPECIFICATION 11
2.3. CENTARL SYSTEM UNIT (CSU) 15
2.3.1. CONNECTION MANAGER 16
2.3.2. DATA ANALYSIS MODULE 17
2.3.3. DATA LOGGER MODULE 24
2.3.4. VISUALIZATION MODULE 25
2.4. TOOLS USED TO DEVELOP BLUEEYES 25
3. SUMMARY 28
4. REFERENCE 29

Please Use Search http://seminarprojects.com/search.php wisely To Get More Information About A Seminar Or Project Topic
18-03-2010, 10:24 PM
Post: #7
RE: Blue eyes (Download Full Report And Abstract)
cool data guys

in fact i had done a paper with this topic and covered the overall working alone in it

now i guess i can get more into the depth.
28-03-2010, 06:41 PM
Post: #8
RE: Blue eyes (Download Full Report And Abstract)

Presented By:
P. Jyothi
LORDS INST OF ENG AND TECH

1. INTRODUCTION
Imagine yourself in a world where humans interact with computers. You are sitting in Front of your personal computer that can listen, talk, or even scream aloud. It has the ability to gather information about you and interact with you through special techniques like facial recognition, speech recognition, etc. It can even understand your emotions at the touch of the mouse. It verifies your identity, feels your presents, and starts interacting with you .You ask the computer to dial to your friend at his office. It realizes the urgency of the situation through the mouse, dials your friend at his office, and establishes a connection.
Human cognition depends primarily on the ability to perceive, interpret, and integrate audio-visuals and sensoring information. Adding extraordinary perceptual abilities to computers would enable computers to work together with human beings as intimate partners. Researchers are attempting to add more capabilities to computers that will allow them to interact like humans, recognize human presents, talk, listen, or even guess their feelings.
The BLUE EYES technology aims at creating computational machines that have perceptual and sensory ability like those of human beings. It uses non-obtrusige sensing method, employing most modern video cameras and microphones to identify the users actions through the use of imparted sensory abilities. The machine can understand what a user wants, where he is looking at, and even realize his physical or emotional states.
Animal survival depends on highly developed sensory abilities. Likewise,
human cognition depends on highly developed abilities to perceive, integrate, and
interpret visual, auditory, and touch information. Without a doubt, computers would be much more powerful if they had even a small fraction of the perceptual ability of animals or humans. Adding such perceptual abilities to computers would enable computers and humans to work together more as partners.Blue Eyes uses sensing technology to identify a user's actions and to extract key information.
This information is then analyzed to determine the user's physical, emotional, or informational state, which in turn can be used to help make the user more productive by performing expected actions or by providing expected information. For example, in future a Blue Eyes-enabled television could become active when the user makes eye contact, at which point the user could then tell the television to "turn on". This paper is about the hardware, software, benefits and interconnection of various parts involved in the blue eye technology. Toward this end, the Blue Eyes aims at creating computational devices with the sort of perceptual abilities that people take for granted Blue eyes is being developed by the team of Poznan University of Technology& Microsoft. It makes use of the blue tooth technology developed by Ericsson.
2. EMOTION MOUSE
One goal of human computer interaction (HCI) is to make an adaptive, smart computer system. This type of project could possibly include gesture recognition, facial recognition, eye tracking, speech recognition, etc. Another non-invasive way to obtain information about a person is through touch. People use their computers to obtain, store and manipulate data using their computer. In order to start creating smart computers, the computer must start gaining information about the user. Our proposed method for gaining user information through touch is via a computer input device, the mouse. From the physiological data obtained from the user, an emotional state may be determined which would then be related to the task the user is currently doing on the computer. Over a period of time, a user model will be built in order to gain a sense of the user's personality. The scope of the project is to have the computer adapt to the user in order to create a better working environment where the user is more productive. The first steps towards realizing this goal are described here
2.1 EMOTION AND COMPUTING
Rosalind Picard (1997) describes why emotions are important to the computing community. There are two aspects of affective computing: giving the computer the ability to detect emotions and giving the computer the ability to express emotions. Not only are emotions crucial for rational decision making as Picard describes, but emotion detection is an important step to an adaptive computer system. An adaptive, smart computer system has been driving our efforts to detect a personâ„¢s emotional state. An important element of incorporating emotion into computing is for productivity for a computer user. A study (Dryer & Horowitz, 1997) has shown that people with personalities that are similar or complement each other collaborate well. Dryer (1999) has also shown that people view their computer as having a personality. For these reasons, it is important to develop computers which can work well with its user.
By matching a personâ„¢s emotional state and the context of the expressed emotion, over a period of time the personâ„¢s personality is being exhibited. Therefore, by giving the computer a longitudinal understanding of the emotional state of its user, the computer could adapt a working style which fits with its userâ„¢s personality. The result of this collaboration could increase productivity for the user. One way of gaining information from a user non-intrusively is by video. Cameras have been used to detect a personâ„¢s emotional state (Johnson, 1999). We have explored gaining information through touch. One obvious place to put sensors is on the mouse. Through observing normal computer usage (creating and editing documents and surfing the web), people spend approximately 1/3 of their total computer time touching their input device. Because of the incredible amount of time spent touching an input device, we will explore the possibility of detecting emotion through touch.
2.2 THEORY
Based on Paul Ekmanâ„¢s facial expression work, we see a correlation between a personâ„¢s emotional state and a personâ„¢s physiological measurements. Selected works from Ekman and others on measuring facial behaviors describe Ekmanâ„¢s Facial Action Coding System (Ekman and Rosenberg, 1997). One of his experiments involved participants attached to devices to record certain measurements including pulse, galvanic skin response (GSR), temperature, somatic movement and blood pressure. He then recorded the measurements as the participants were instructed to mimic facial expressions which corresponded to the six basic emotions. He defined the six basic emotions as anger, fear, sadness, disgust, joy and surprise. From this work, Dryer (1993) determined how physiological measures could be used to distinguish various emotional states.
Six participants were trained to exhibit the facial expressions of the six basic emotions. While each participant exhibited these expressions, the physiological changes associated with affect were assessed. The measures taken were GSR, heart rate, skin temperature and general somatic activity (GSA). These data were then subject to two analyses. For the first analysis, a multidimensional scaling (MDS) procedure was used to determine the dimensionality of the data. This analysis suggested that the physiological similarities and dissimilarities of the six emotional states fit within a four dimensional model. For the second analysis, a discriminant function analysis was used to determine the mathematic functions that would distinguish the six emotional states. This analysis suggested that all four physiological variables made significant, non-redundant contributions to the functions that distinguish the six states. Moreover, these analyses indicate that these four physiological measures are sufficient to determine reliably a personâ„¢s specific emotional state. Because of our need to incorporate these measurements into a small, non-intrusive form, we will explore taking these measurements from the hand. The amount of conductivity of the skin is best taken from the fingers. However, the other measures may not be as obvious or robust. We hypothesize that changes in the temperature of the finger are reliable for prediction of emotion. We also hypothesize the GSA can be measured by change in movement in the computer mouse. Our efforts to develop a robust pulse meter are not discussed here.
2.3 EXPERIMENTAL DESIGN
An experiment was designed to test the above hypotheses. The four physiological
Readings measured were heart rate, temperature, GSR and somatic movement. The heart rate was measured through a commercially available chest strap sensor. The temperature was measured with a thermocouple attached to a digital multimeter (DMM). The GSR was also measured with a DMM. The somatic movement was measured by recording the computer mouse movements.
2.3.1 Method
Six people participated in this study (3 male, 3 female). The experiment was within subject design and order of presentation was counter-balanced across participants.
2.3.2 Procedure
Participants were asked to sit in front of the computer and hold the temperature and GSR sensors in their left hand hold the mouse with their right hand and wore the chest sensor. The resting (baseline) measurements were recorded for five minutes and then the participant was instructed to act out one emotion for five minutes. The emotions consisted of: anger, fear, sadness, disgust, happiness and surprise. The only instruction for acting out the emotion was to show the emotion in their facial expressions.
2.3.3 Results
The data for each subject consisted of scores for four physiological assessments [GSA, GSR, pulse, and skin temperature, for each of the six emotions (anger, disgust, fear, happiness, sadness, and surprise)] across the five minute baseline and test sessions. GSA data was sampled 80 times per second, GSR and temperature were reported approximately 3-4 times per second and pulse was recorded as a beat was detected, approximately 1 time per second. We first calculated the mean score for each of the baseline and test sessions. To account for individual variance in physiology, we calculated the difference between the baseline and test scores. Scores that differed by more than one and a half standard deviations from the mean were treated as missing. By this criterion, twelve score were removed from the analysis. The remaining data are described in Table 1.
In order to determine whether our measures of physiology could discriminate among the six different emotions, the data were analyzed with a discriminant function analysis. The four physiological difference scores were the discriminating variables and the six emotions were the discriminated groups. The variables were entered into the equation simultaneously, and four canonical discriminant functions were calculated. A Wilksâ„¢ Lambda test of these four functions was marginally statistically significant; for lambda = .192, chi-square (20) = 29.748, p < .075. The functions are shown in Table 2.
The un standardized canonical discriminant functions evaluated at group means are shown in Table 3. Function 1 is defined by sadness and fear at one end and anger and surprise at the other. Function 2 has fear and disgust at one end and sadness at the other. Function 3 has happiness at one end and surprise at the other. Function 4 has disgust and anger at one end and surprise at the other. Table 3:
To determine the effectiveness of these functions, we used them to predict the group membership for each set of physiological data. As shown in Table 4, two-thirds of the cases were successfully classified
The results show the theory behind the Emotion mouse work is fundamentally sound. The physiological measurements were correlated to emotions using a correlation model. The correlation model is derived from a calibration process in which a baseline attribute to emotion correlation is rendered based on statistical analysis of calibration signals generated by users having emotions that are measured or otherwise known at calibration time. Now that we have proven the method, the next step is to improve the hardware. Instead of using cumbersome multimeters to gather information about the user, it will be better to use smaller and less intrusive units. We plan to improve our infrared pulse detector which can be placed inside the body of the mouse. Also, a framework for the user modeling needs to be developed in order to correctly handle all of the information after it has been gathered. There are other possible applications for the Emotion technology other than just increased productivity for a desktop computer user. Other domains such as entertainment, health and the communications and the automobile industry could find this technology useful for other purposes.
3. MANUAL AND GAZE INPUT CASCADED (MAGIC)
POINTING
This work explores a new direction in utilizing eye gaze for computer input. Gaze tracking has long been considered as an alternative or potentially superior pointing method for computer input. We believe that many fundamental limitations exist with traditional gaze pointing. In particular, it is unnatural to overload a perceptual channel such as vision with a motor control task. We therefore propose an alternative approach, dubbed MAGIC (Manual And Gaze Input Cascaded) pointing. With such an approach, pointing appears to the user to be a manual task, used for fine manipulation and selection. However, a large portion of the cursor movement is eliminated by warping the cursor to the eye gaze area, which encompasses the target. Two specific MAGIC pointing techniques, one conservative and one liberal, were designed, analyzed, and implemented with an eye tracker we developed. They were then tested in a pilot study. This early stage exploration showed that the MAGIC pointing techniques might offer many advantages, including reduced physical effort and fatigue as compared to traditional manual pointing, greater accuracy and naturalness than traditional gaze pointing, and possibly faster speed than manual pointing. The pros and cons of the two techniques are discussed in light of both performance data and subjective reports. In our view, there are two fundamental shortcomings to the existing gaze pointing techniques, regardless of the maturity of eye tracking technology. First, given the one-degree size of the fovea and the subconscious jittery motions that the eyes constantly produce, eye gaze is not precise enough to operate UI widgets such as scrollbars, hyperlinks, and slider handles In Proc. CHIâ„¢99: ACM Conference on Human Factors in Computing Systems. 246-253, Pittsburgh, 15-20 May1999 Copyright ACM 1999 0-201-48559-1/99/05...$5.00 on todayâ„¢s GUI interfaces. At a 25-inch viewing distance to the screen, one degree of arc corresponds to 0.44 in, which is twice the size of a typical scroll bar and much greater than the size of a typical character.
Second, and perhaps more importantly, the eye, as one of our primary perceptual devices, has not evolved to be a control organ. Sometimes its movements are voluntarily controlled while at other times it is driven by external events. With the target selection by dwell time method, considered more natural than selection by blinking [7], one has to be conscious of where one looks and how long one looks at an object. If one does not look at a target continuously for a set threshold (e.g., 200 ms), the target will not be successfully selected. On the other hand, if one stares at an object for more than the set threshold, the object will be selected, regardless of the userâ„¢s intention. In some cases there is not an adverse effect to a false target selection. Other times it can be annoying and counter-productive (such as unintended jumps to a web page). Furthermore, dwell time can only substitute for one mouse click. There are often two steps to target activation. A single click selects the target (e.g., an application icon) and a double click (or a different physical button click) opens the icon (e.g., launches an application). To perform both steps with dwell time is even more difficult. In short, to load the visual perception channel with a motor control task seems fundamentally at odds with usersâ„¢ natural mental model in which the eye searches for and takes in information and the hand produces output that manipulates external objects. Other than for disabled users, who have no alternative, using eye gaze for practical pointing does not appear to be very promising.
Are there interaction techniques that utilize eye movement to assist the control task but do not force the user to be overly conscious of his eye movement We wanted to design a technique in which pointing and selection remained primarily a manual control task but were also aided by gaze tracking. Our key idea is to use gaze to dynamically redefine (warp) the home position of the pointing cursor to be at the vicinity of the target, which was presumably what the user was looking at, thereby effectively reducing the cursor movement amplitude needed for target selection.
Once the cursor position had been redefined, the user would need to only make a small movement to, and click on, the target with a regular manual input device. In other words, we wanted to achieve Manual And Gaze Input Cascaded (MAGIC) pointing, or Manual Acquisition with Gaze Initiated Cursor. There are many different ways of designing a MAGIC pointing technique. Critical to its effectiveness is the identification of the target the user intends to acquire. We have designed two MAGIC pointing techniques, one liberal and the other conservative in terms of target identification and cursor placement. The liberal approach is to warp the cursor to every new object the user looks at (See Figure 1).
The user can then take control of the cursor by hand near (or on) the target, or ignore it and search for the next target. Operationally, a new object is defined by sufficient distance (e.g., 120 pixels) from the current cursor position, unless the cursor is in a controlled motion by hand. Since there is a 120-pixel threshold, the cursor will not be warped when the user does continuous manipulation such as drawing. Note that this MAGIC pointing technique is different from traditional eye gaze control, where the user uses his eye to point at targets either without a cursor or with a cursor that constantly follows the jittery eye gaze motion.
The liberal approach may appear pro-active, since the cursor waits readily in the vicinity of or on every potential target. The user may move the cursor once he decides to acquire the target he is looking at. On the other hand, the user may also feel that the cursor is over-active when he is merely looking at a target, although he may gradually adapt to ignore this behavior. The more conservative MAGIC pointing technique we have explored does not warp a cursor to a target until the manual input device has been actuated. Once the manual input device has been actuated, the cursor is warped to the gaze area reported by the eye tracker. This area should be on or in the vicinity of the target. The user would then steer the cursor annually towards the target to complete the target acquisition. As illustrated in Figure 2, to minimize directional uncertainty after the cursor appears in the conservative technique, we introduced an intelligent bias. Instead of being placed at the enter of the gaze area, the cursor position is offset to the intersection of the manual actuation vector and the boundary for the gaze area. This means that once warped, the cursor is likely to appear in motion towards the target, regardless of how the user actually actuated the manual input device. We hoped that with the intelligent bias the user would not have to Gaze position reported by eye tracker Eye tracking boundary with 95% confidence True target will be within the circle with 95% probability. The cursor is warped to eye tracking position, which is on or near the true target Previous cursor position, far from target (e.g., 200 pixels) Figure 1.
The liberal MAGIC pointing technique: cursor is placed in the vicinity of a target that the user fixates on. Actuate input device, observe the cursor position and decide in which direction to steer the cursor. The cost to this method is the increased manual movement amplitude. Figure 2. The conservative MAGIC pointing technique with intelligent offset To initiate a pointing trial, there are two strategies available to the user. One is to follow virtual inertia: move from the cursorâ„¢s current position towards the new target the user is looking at. This is likely the strategy the user will employ, due to the way the user interacts with todayâ„¢s interface. The alternative strategy, which may be more advantageous but takes time to learn, is to ignore the previous cursor position and make a motion which is most convenient and least effortful to the user for a given input device.
The goal of the conservative MAGIC pointing method is the following. Once the user looks at a target and moves the input device, the cursor will appear out of the blue in motion towards the target, on the side of the target opposite to the initial actuation vector. In comparison to the liberal approach, this conservative approach has both pros and cons. While with this technique the cursor would never be over-active and jump to a place the user does not intend to acquire, it may require more hand-eye coordination effort. Both the liberal and the conservative MAGIC pointing techniques offer the following potential advantages:
1. Reduction of manual stress and fatigue, since the cross screen long-distance cursor
movement is eliminated from manual control.
2. Practical accuracy level. In comparison to traditional pure gaze pointing whose accuracy is fundamentally limited by the nature of eye movement, the MAGIC pointing techniques let the hand complete the pointing task, so they can be as accurate as any other manual input techniques.
3. A more natural mental model for the user. The user does not have to be aware of the role of the eye gaze. To the user, pointing continues to be a manual task, with a cursor conveniently appearing where it needs to be.
4. Speed. Since the need for large magnitude pointing operations is less than with pure
manual cursor control, it is possible that MAGIC pointing will be faster than pure manual pointing.
5. Improved subjective speed and ease-of-use. Since the manual pointing amplitude is
smaller, the user may perceive the MAGIC pointing system to operate faster and more
pleasantly than pure manual control, even if it operates at the same speed or more slowly.
The fourth point wants further discussion. According to the well accepted Fittsâ„¢ Law, manual pointing time is logarithmically proportional to the A/W ratio, where A is the movement distance and W is the target size. In other words, targets which are smaller or farther away take longer to acquire. For MAGIC pointing, since the target size remains the same but the cursor movement distance is shortened, the pointing time can hence be reduced. It is less clear if eye gaze control follows Fittsâ„¢ Law. In Ware and Mikaelianâ„¢s study, selection time was shown to be logarithmically proportional to target distance, thereby conforming to Fittsâ„¢ Law. To the contrary, Silbert and Jacob [9] found that trial completion time with eye tracking input increases little with distance, therefore defying Fittsâ„¢ Law. In addition to problems with todayâ„¢s eye tracking systems, such as delay, error, and inconvenience, there may also be many potential human factor disadvantages to the MAGIC pointing techniques
we have proposed, including the following:
1. With the more liberal MAGIC pointing technique, the cursor warping can be overactive at times, since the cursor moves to the new gaze location whenever the eye
gaze moves more than a set distance (e.g., 120 pixels) away from the cursor. This could be particularly distracting when the user is trying to read. It is possible to introduce additional constraint according to the context. For example, when the userâ„¢s eye appears to follow a text reading pattern, MAGIC pointing can be automatically suppressed.
2. With the more conservative MAGIC pointing technique, the uncertainty of the exact location at which the cursor might appear may force the user, especially a novice, to Adopt a cumbersome strategy: take a touch (use the manual input device to activate the cursor), wait (for the cursor to appear), and move (the cursor to the target manually). Such a strategy may prolong the target acquisition time. The user may have to learn a novel hand-eye coordination pattern to be efficient with this technique. Gaze position reported by eye tracker Eye tracking boundary with 95% confidence True target will be within the circle with 95% probability The cursor is warped to the boundary of the gaze area, along the initial actuation vector Previous cursor position, far from target Initial manual actuation vector.
3. With pure manual pointing techniques, the user, knowing the current cursor location, could conceivably perform his motor acts in parallel to visual search. Motor action may start as soon as the userâ„¢s gaze settles on a target. With MAGIC pointing techniques, the motor action computation (decision) cannot start until the cursor appears. This may negate the time saving gained from the MAGIC pointing techniqueâ„¢s reduction of movement amplitude. Clearly, experimental (implementation and empirical) work is needed to validate, refine, or invent alternative MAGIC pointing techniques.
3.1 IMPLEMENTATION
We took two engineering efforts to implement the MAGIC pointing techniques. One was to design and implement an eye tracking system and the other was to implement MAGIC pointing techniques at the operating systems level, so that the techniques can work with all software applications beyond demonstration software.
3.2 THE IBM ALMADEN EYE TRACKER
Since the goal of this work is to explore MAGIC pointing as a user interface technique, we started out by purchasing a commercial eye tracker (ASL Model 5000) after a market survey. In comparison to the system reported in early studies (e.g. [7]), this system is much more compact and reliable. However, we felt that it was still not robust enough for a variety of people with different eye characteristics, such as pupil brightness and correction glasses. We hence chose to develop and use our own eye tracking system [10]. Available commercial systems, such as those made by ISCAN Incorporated, LC Technologies, and Applied Science Laboratories (ASL), rely on a single light source that is positioned either off the camera axis in the case of the ISCANETL-400 systems, or on-axis in the case of the LCT and the ASL E504 systems. Illumination from an off-axis source (or ambient illumination) generates a dark pupil image.
When the light source is placed on-axis with the camera optical axis, the camera is able to detect the light reflected from the interior of the eye, and the image of the pupil appears bright (see Figure 3).
This effect is often seen as the red-eye in flash photographs when the flash is close to the camera lens.
Bright (left) and dark (right) pupil images resulting from on- and off-axis illumination. The glints, or corneal reflections, from the on- and off-axis light sources can be easily identified as the bright points in the iris. The Almaden system uses two near infrared (IR) time multiplexed light sources, composed of two sets of IR LED's, which were synchronized with the camera frame rate. One light source is placed very close to the camera's optical axis and is synchronized with the even frames. Odd frames are synchronized with the second light source, positioned off axis. The two light sources are calibrated to provide approximately equivalent whole-scene illumination. Pupil detection is realized by means of subtracting the dark pupil image from the bright pupil image. After thresholding the difference, the largest connected component is identified as the pupil. This technique significantly increases the robustness and reliability of the eye tracking system. After implementing our system with satisfactory results, we discovered that similar pupil detection schemes had been independently developed by Tomonoetal and Ebisawa and Satoh.
It is unfortunate that such a method has not been used in the commercial systems. We recommend that future eye tracking product designers consider such an approach.
Once the pupil has been detected, the corneal reflection (the glint reflected from the
surface of the cornea due to one of the light sources) is determined from the dark pupil image. The reflection is then used to estimate the user's point of gaze in terms of the screen coordinates where the user is looking at. The estimation of the user's gaze requires an initial calibration procedure, similar to that required by commercial eye trackers. Our system operates at 30 frames per second on a Pentium II 333 MHz machine running Windows NT. It can work with any PCI frame grabber compatible with Video for Windows.
3.3 IMPLIMENTING MAGIC POINTING
We programmed the two MAGIC pointing techniques on a Windows NT system. The techniques work independently from the applications. The MAGIC pointing program takes data from both the manual input device (of any type, such as a mouse) and the eye tracking system running either on the same machine or on another machine connected via serial port. Raw data from an eye tracker can not be directly used for gaze-based interaction, due to noise from image processing, eye movement jitters, and samples taken during saccade (ballistic eye movement) periods. We experimented with various filtering techniques and found the most effective filter in our case is similar to that described in [7]. The goal of filter design in general is to make the best compromise between preserving signal bandwidth and eliminating unwanted noise. In the case of eye tracking, as Jacob argued, eye information relevant to interaction lies in the fixations. The key is to select fixation points with minimal delay. Samples collected during a saccade are unwanted and should be avoided. In designing our algorithm for picking points of fixation, we considered our tracking system speed (30 Hz), and that the MAGIC pointing techniques utilize gaze information only once for each new target, probably immediately after a saccade. Our filtering algorithm was designed to pick a fixation with minimum delay by means of selecting two adjacent points over two samples.
3.4 EXPERIMENT
Empirical studies, are relatively rare in eye tracking-based interaction research, although they are particularly needed in this field. Human behavior and processes at the perceptual motor level often do not conform to conscious-level reasoning. One usually cannot correctly describe how to make a turn on a bicycle. Hypotheses on novel interaction techniques can only be validated by empirical data. However, it is also particularly difficult to conduct empirical research on gaze-based interaction techniques, due to the complexity of eye movement and the lack of reliability in eye tracking equipment. Satisfactory results only come when everything is going right. When results are not as expected, it is difficult to find the true reason among many possible reasons: Is it because a subjectâ„¢s particular eye property fooled the eye tracker Was there a calibration error Or random noise in the imaging system Or is the hypothesis in fact invalid We are still at a very early stage of exploring the MAGIC pointing techniques. More refined or even very different techniques may be designed in the future. We are by no means ready to conduct the definitive empirical studies on MAGIC pointing. However, we also feel that it is important to subject our work to empirical evaluations early so that quantitative observations can be made and fed back to the iterative design-evaluation-design cycle. We therefore decided to conduct a small-scale pilot study to take an initial peek at the use of MAGIC pointing, however unrefined.
3.5 EXPERIMENTAL DESIGN
The two MAGIC pointing techniques described earlier were put to test using a set of parameters such as the filterâ„¢s temporal and spatial thresholds, the minimum cursor warping distance, and the amount of intelligent bias (subjectively selected by the authors without extensive user testing). Ultimately the MAGIC pointing techniques should be evaluated with an array of manual input devices, against both pure manual and pure gaze-operated pointing methods.
Since this is an early pilot study, we decided to limit ourselves to one manual input device. A standard mouse was first considered to be the manual input device in the experiment. However, it was soon realized not to be the most suitable device for MAGIC pointing, especially when a user decides to use the push-upwards strategy with the intelligent offset. Because in such a case the user always moves in one direction, the mouse tends to be moved off the pad, forcing the user adjust the mouse position, often during a pointing trial. We hence decided to use a miniature isometric pointing stick (IBM Track Point IV, commercially used in the IBM ThinkPad 600 and 770 series notebook computers). Another device suitable for MAGIC pointing is a touchpad: the user can choose one convenient gesture and to take advantage of the intelligent offset. The experimental task was essentially a Fittsâ„¢ pointing task. Subjects were asked to point and click at targets appearing in random order. If the subject clicked off-target, a miss was logged but the trial continued until a target was clicked. An extra trial was added to make up for the missed trial. Only trials with no misses were collected for time performance analyses. Subjects were asked to complete the task as quickly as possible and as accurately as possible. To serve as a motivator, a $20 cash prize was set for the subject with the shortest mean session completion time with any technique.
The task was presented on a 20 inch CRT color monitor, with a 15 by 11 inch viewable area set at resolution of 1280 by 1024 pixels. Subjects sat from the screen at a distance of 25 inches. The following factors were manipulated in the experiments:
¢ Two target sizes: 20 pixels (0.23 in or 0.53 degree of viewing angle at 25 in distance) and 60 pixels in diameter (0.7 in, 1.61 degree)
¢ Three target distances: 200 pixels (2.34 in, 5.37 degree), 500 pixels (5.85 in, 13.37 degree), and 800 pixels (9.38 in, 21.24 degree)
¢ Three pointing directions: horizontal, vertical and diagonal
A within-subject design was used. Each subject performed the task with all three techniques: (1) Standard, pure manual pointing with no gaze tracking (No Gaze); (2) The conservative MAGIC pointing method with intelligent offset (Gaze1); (3) The liberal MAGIC pointing method (Gaze2). Nine subjects, seven male and two female, completed the experiment. The order of techniques was balanced by a Latin square pattern. Seven subjects were experienced Track Point users, while two had little or no experience. With each technique, a 36-trial practice session was first given, during which subjects were encouraged to explore and to find the most suitable strategies (aggressive, gentle, etc.). The practice session was followed by two data collection sessions. Although our eye tracking system allows head motion, at least for those users who do not wear glasses, we decided to use a chin rest to minimize instrumental error.
3.6 EXPERIMENTAL RESULTS
Given the pilot nature and the small scale of the experiment, we expected the statistical power of the results to be on the weaker side. In other words, while the significant effects revealed are important, suggestive trends that are statistically non-significant are still worth noting for future research. First, we found that subjectsâ„¢ trial completion time significantly varied with techniques: F(2, 16) = 6.36, p < 0.01.
The total average completion time was 1.4 seconds with the standard manual control
technique 1.52 seconds with the conservative MAGIC pointing technique (Gaze1), and 1.33 seconds with the liberal MAGIC pointing technique (Gaze2). Note that the Gaze1 Technique had the greatest improvement from the first to the second experiment session, suggesting the possibility of matching the performance of the other two techniques with further practice. As expected, target size significantly influenced pointing time: F(1,8) = 178, p < 0.001. This was true for both the manual and the two MAGIC pointing techniques (Figure 6).
Pointing amplitude also significantly affected completion time: F(2, 8) = 97.5, p < 0.001. However, the amount of influence varied with the technique used, as indicated by the significant interaction between technique and amplitude: F(4, 32) = 7.5, p < 0.001 (Figure 7).

As pointing amplitude increased from 200 pixels to 500 pixels and then to 800 pixels, subjectsâ„¢ completion time with the No_Gaze condition increased in a non-linear, logarithmic-like pace as Fittsâ„¢ Law predicts. This is less true with the two MAGIC pointing techniques, particularly the Gaze2 condition, which is definitely not logarithmic. Nonetheless, completion time with the MAGIC pointing techniques did increase as target distance increased. This is intriguing because in MAGIC pointing techniques, the manual control portion of the movement should be the distance from the warped cursor position to the true target. Such distance depends on eye tracking system accuracy, which is unrelated to the previous cursor position.
In short, while completion time and target distance with the MAGIC pointing techniques did not completely follow Fittsâ„¢ Law, they were not completely independent either. Indeed, when we lump target size and target distance according to the Fittsâ„¢ Law
Index of Difficulty ID = log2(A/W + 1) [15],
we see a similar phenomenon. For the No_Gaze condition:
T = 0.28 + 0.31 ID (r=0.912)
The particular settings of our experiment were very different from those typically reported in a Fittsâ„¢ Law experiment: to simulate more realistic tasks we used circular targets distributed in varied directions in a randomly shuffled order, instead of two vertical bars displaced only in the horizontal dimension. We also used an isometric pointing stick, not a mouse. Considering these factors, the above equation is reasonable.The index of performance (IP) was 3.2 bits per second, in comparison to the 4.5 bits per
second in a typical setting (repeated mouse clicks on two vertical bars) [16].
For the Gaze1 condition:
T = 0.8 + 0.22 ID (r=0.716)
IP = 4.55 bits per second
For the Gaze1 condition:
T = 0.8 + 0.22 ID (r=0.716)
IP = 4.55 bits per second
Note that the data from the two MAGIC pointing techniques fit the Fittsâ„¢ Law model relatively poorly (as expected), although the indices of performance (4.55 and 4.76 bps) were much higher than the manual condition (3.2 bps).
Finally, Figure 8 shows that the angle at which the targets
were presented had little influence on trial completion time: F(2, 16) = 1.57, N.S.
The number of misses (clicked off target) was also analyzed. The only significant factor to the number of misses is target size: F(1,8) = 15.6, p < 0.01. Users tended to have more misses with small targets. More importantly, subjects made no more misses with the MAGIC pointing techniques than with the pure manual technique (No_Gaze “ 8.2 %, Gaze1 “7%, Gaze2 “ 7.5%).
4. ARTIFICIAL INTELLIGENT SPEECH RECOGNITION
It is important to consider the environment in which the speech recognition system has to work. The grammar used by the speaker and accepted by the system, noise level, noise type, position of the microphone, and speed and manner of the userâ„¢s speech are some factors that may affect the quality of speech recognition .When you dial the telephone number of a big company, you are likely to hear the sonorous voice of a cultured lady who responds to your call with great courtesy saying Welcome to company X. Please give me the extension number you want. You pronounce the extension number, your name, and the name of person you want to contact. If the called person accepts the call, the connection is given quickly. This is artificial intelligence where an automatic callhandling system is used without employing any telephone operator.
4.1 THE TECHNOLOGY
Artificial intelligence (AI) involves two basic ideas. First, it involves studying the thought processes of human beings. Second, it deals with representing those processes via machines (like computers, robots, etc). AI is behavior of a machine, which, if performed by a human being, would be called intelligent. It makes machines smarter and more useful, and is less expensive than natural intelligence. Natural language processing (NLP) refers to artificial intelligence methods of communicating with a computer in a natural language like English. The main objective of a NLP program is to understand input and initiate action. The input words are scanned and matched against internally stored known words. Identification of a key word causes some action to be taken. In this way, one can communicate with the computer in oneâ„¢s language. No special commands or computer language are required. There is no need to enter programs in a special language for creating software.
4.2 SPEECH RECOGNITION
The user speaks to the computer through a microphone, which, in used; a simple system may contain a minimum of three filters. The more the number of filters used, the higher the probability of accurate recognition. Presently, switched capacitor digital filters are used because these can be custom-built in integrated circuit form. These are smaller and cheaper than active filters using operational amplifiers. The filter output is then fed to the ADC to translate the analogue signal into digital word. The ADC samples the filter outputs many times a second. Each sample represents different amplitude of the signal .Evenly spaced vertical lines represent the amplitude of the audio filter output at the instant of sampling. Each value is then converted to a binary number proportional to the amplitude of the sample. A central processor unit (CPU) controls the input circuits that are fed by the ADCS. A large RAM (random access memory) stores all the digital values in a buffer area. This digital information, representing the spoken word, is now accessed by the CPU to process it further. The normal speech has a frequency range of 200 Hz to 7 Hz. Recognizing a telephone call is more difficult as it has bandwidth limitation of 300 Hz to3.3 kHz.
As explained earlier, the spoken words are processed by the filters and ADCs. The binary representation of each of these words becomes a template or standard, against which the future words are compared. These templates are stored in the memory. Once the storing process is completed, the system can go into its active mode and is capable of identifying spoken words. As each word is spoken, it is converted into binary equivalent and stored in RAM. The computer then starts searching and compares the binary input pattern with the templates. t is to be noted that even if the same speaker talks the same text, there are always slight variations in amplitude or loudness of the signal, pitch, frequency difference, time gap, etc. Due to this reason, there is never a perfect match between the template and binary input word. The pattern matching process therefore uses statistical techniques and is designed to look for the best fit.
The values of binary input words are subtracted from the corresponding values in the
templates. If both the values are same, the difference is zero and there is perfect match. If not, the subtraction produces some difference or error. The smaller the error, the better the match . When the best match occurs, the word is identified and displayed on the screen or used in some other manner. The search process takes a considerable amount of time, as the CPU has to make many comparisons before recognition occurs. This necessitates use of very high-speed processors. A large RAM is also required as even though a spoken word may last only a few hundred milliseconds, but the same is translated into many thousands of digital words. It is important to note that alignment of words and templates are to be matched correctly in time, before computing the similarity score. This process, termed as dynamic time warping, recognizes that different speakers pronounce the same words at different speeds as well as elongate different parts of the same word. This is important for the speaker-independent recognizers.
4.3 APPLICATIONS
One of the main benefits of speech recognition system is that it lets user do other works simultaneously. The user can concentrate on observation and manual operations, and still control the machinery by voice input commands. Another major application of speech processing is in military operations. Voice control of weapons is an example. With reliable speech recognition equipment, pilots can give commands and information to the computers by simply speaking into their microphones”they don™t have to use their hands for this purpose. Another good example is a radiologist scanning hundreds of Xrays, ultrasonograms, CT scans and simultaneously dictating conclusions to a speech recognition system connected to word processors. The radiologist can focus his attention on the images rather than writing the text. Voice recognition could also be used on computers for making airline and hotel reservations. A user requires simply to state his needs, to make reservation, cancel a reservation, or make enquiries about schedule.
5. THE SIMPLE USER INTERST TRACKER (SUITOR)
Computers would have been much more powerful, had they gained perceptual and sensory abilities of the living beings on the earth. What needs to be developed is an intimate relationship between the computer and the humans. And the Simple User Interest Tracker (SUITOR) is a revolutionary approach in this direction.
By observing the Webpage a netizen is browsing, the SUITOR can help by fetching more information at his desktop. By simply noticing where the userâ„¢s eyes focus on the computer screen, the SUITOR can be more precise in determining his topic of interest. It can even deliver relevant information to a handheld device. The success lies in how much the suitor can be intimate to the user. IBM's BlueEyes research project began with a simple question, according to Myron Flickner, a manager in Almaden's USER group: Can we exploit nonverbal cues to create more effective user interfaces
One such cue is gaze”the direction in which a person is looking. Flickner and his colleagues have created some new techniques for tracking a person's eyes and have incorporated this gaze-tracking technology into two prototypes. One, called SUITOR (Simple User Interest Tracker), fills a scrolling ticker on a computer screen with information related to the user's current task. SUITOR knows where you are looking, what applications you are running, and what Web pages you may be browsing. "If I'm reading a Web page about IBM, for instance," says Paul Maglio, the Almaden cognitive scientist who invented SUITOR, "the system presents the latest stock price or business news stories that could affect IBM. If I read the headline off the ticker, it pops up the story in a browser window. If I start to read the story, it adds related stories to the ticker. That's the whole idea of an attentive system”one that attends to what you are doing, typing, reading, so that it can attend to your information needs."
6. PARTS OF A BLUE EYE SYSTEM
The major parts in the Blue eye system are Data Acquisition Unit and Central System Unit. The tasks of the mobile Data Acquisition Unit are to maintain Bluetooth connections, to get information from the sensor and sending it over the Wireless connection, to deliver the alarm messages sent from the Central System Unit to the operator and handle personalized ID cards.Central System Unit maintains the other side of the Bluetooth connection, buffers incoming sensor data, performs on-line data analysis, records the conclusions for further exploration and provides visualization interface.
THE HARDWARE:
Data Acquisition Unit:
Data Acquisition Unit is a mobile part of the Blue eyes system. Its main task is to fetch the physiological data from the sensor and to send it to the central system to be processed. To accomplish the task the device must manage wireless Bluetooth connections (connection establishment, authentication and termination). Personal ID cards and PIN codes provide operator's authorization. Communication with the operator is carried on using a simple 5-key keyboard, a small LCD display and a beeper. When an exceptional situation is detected the device uses them to notify the operator. Voice data is transferred using a small headset, interfaced to the DAU with standard mini-jack plugs.
The Data Acquisition Unit comprises several hardware modules
Atmel 89C52 microcontroller - system core
Bluetooth module (based on ROK101008)
HD44780 - small LCD display
24C16 - I2C EEPROM (on a removable ID card)
MC145483 “ 13bit PCM codec
Jazz Multisensor interface
beeper and LED indicators, 6 AA batteries and voltage level monitor
CENTRAL SYSTEM UNIT:
Central System Unit hardware is the second peer of the wireless connection. The box contains a Bluetooth module (based on ROK101008) and a PCM codec for voice data transmission. The module is interfaced to a PC using a parallel, serial and USB cable. The audio data is accessible through standard minijack sockets .
To program operator's personal ID cards we developed a simple programming device. The programmer is interfaced to a PC using serial and PS/2 (power source) ports. Inside, there is Atmel 89C2051 microcontroller, which handles UART transmission and I2C EEPROM (ID card) programming.
THE SOFTWARE:
Blue Eyes software's main task is to look after working operators' physiological condition. To assure instant reaction on the operators' condition change the software performs real time buffering of the incoming data, real-time physiological data analysis and alarm triggering. The Blue Eyes software comprises several functional modules System core facilitates the transfers flow between other system modules (e.g. transfers raw data from the Connection Manager to data analyzers, processed data from the data analyzers to GUI controls, other data analyzers, data logger etc.). The System Core fundamental are single-producer-multi-consumer thread safe queues. Any number of consumers can register to receive the data supplied by a producer. Every single consumer can register at any number of producers, receiving therefore different types of data. Naturally, every consumer may be a producer for other consumers. This approach enables high system scalability “
new data processing modules (i.e. filters, data analyzers and loggers) can be easily added by simply registering as a costumer.
Connection Manager is responsible for managing the wireless communication between the mobile Data Acquisition Units and the central system. The Connection Manager handles:
communication with the CSU hardware
searching for new devices in the covered range
establishing Bluetooth connections
connection authentication
incoming data buffering
sending alerts
Data Analysis module performs the analysis of the raw sensor data in order to
obtain information about the operatorâ„¢s physiological condition. The separately
running Data Analysis module supervises each of the working operators.
The module consists of a number of smaller analyzers extracting different
types of information. Each of the analyzers registers at the appropriate Operator Manager or another analyzer as a data consumer and, acting as a producer,
provides the results of the analysis. The most important analyzers are:
saccade detector - monitors eye movements in order to determine the level of operator's visual attention
pulse rate analyzer - uses blood oxygenation signal to compute operator's pulse rate
custom analyzers - recognize other behaviors than those which are built-in the
system. The new modules are created using C4.5 decision tree induction algorithm
Visualization module provides a user interface for the supervisors. It enables them to watch each of the working operatorâ„¢s physiological condition along with a preview of selected video source and related sound stream. All the incoming alarm messages are instantly signaled to the supervisor. The Visualization module can be set in an off-line mode, where all the data is fetched from the database. Watching all the recorded physiological parameters, alarms, video and audio data the supervisor is able to reconstruct the course of the selected operatorâ„¢s duty.
The physiological data is presented using a set of custom-built GUI controls:
a pie-chart used to present a percentage of time the operator was actively
acquiring the visual information
A VU-meter showing the present value of a parameter time series
displaying a history of selected parameters' value.
7 .CONCLUSION
The nineties witnessed quantum leaps interface designing for improved man machine interactions. The BLUE EYES technology ensures a convenient way of simplifying the life by providing more delicate and user friendly facilities in computing devices. Now that we have proven the method, the next step is to improve the hardware. Instead of using cumbersome modules to gather information about the user, it will be better to use smaller and less intrusive units. The day is not far when this technology will push its way into your house hold, making you more lazy. It may even reach your hand held mobile device. Any way this is only a technological forecast.
8. REFERENCES
1. http://www.ieee.org.com
2. http://www.umtsworld.com/technology/spreading.html
3. http://www.bluenetworks.or/nemo/drafts
4. http://www.estoile.com/links/ipsec.htm
5. A.jajszczyk,automatically switched blue eyes networks: Benefits and
Requirement,IEEE blue toooth.feb 2005, vol 3, no1, pp.
6. A .Banerjee,Generalized multi protocol label switching: an over view of
computer enhancements and recovery techniques,IEEE commun. Mag., vol 36
7. J.jones, L.ong, and m.lazer,creating and intelligent technology
network/worldwide interoperability demonstration.IEEEcommun .mag. vol 42.
8. PDF created with PDF Factory Pro trial version http://www.software-partners.co.uk


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01-04-2010, 10:31 PM
Post: #9
RE: Blue eyes (Download Full Report And Abstract)
BLUE EYES TECHNOLOGY
ABSTRACT:
Is it possible to create a computer which can interact with us as we interact each other? For example imagine in a fine morning you walk on to your computer room and switch on your computer, and then it tells you Hey friend, good morning you seem to be a bad mood today. And then it opens your mailbox and shows you some of the mails and tries to cheer you. It seems to be a fiction, but it will be the life lead by BLUE EYES in the very near future. The basic idea behind this technology is to give the computer the human power. We all have some perceptual abilities. That is we can understand each others feelings. For example we can understand ones emotional state by analyzing his facial expression. If we add these perceptual abilities of human to computers would enable computers to work together with human beings as intimate partners. The BLUE EYES technology aims at creating computational machines that have perceptual and sensory ability like those of human beings.
How can we make computers "see" and "feel"?
Blue Eyes uses sensing technology to identify a user's actions and to extract key information. This information is then analyzed to determine the user's physical, emotional, or informational state, which in turn can be used to help make the user more productive by performing expected actions or by providing expected information. For example, in future a Blue Eyes-enabled television could become active when the user makes eye contact, at which point the user could then tell the television to "turn on". This paper is about the hardware, software, benefits and interconnection of various parts involved in the blue eye technology.
INTRODUCTION:
Animal survival depends on highly developed sensory abilities. Likewise, human cognition depends on highly developed abilities to perceive, integrate, and interpret visual, auditory, and touch information. Without a doubt, computers would be much more
powerful if they had even a small fraction of the perceptual ability of animals or humans. Adding such perceptual abilities to computers would enable computers and humans to work together more as partners. Toward this end, the Blue Eyes aims at creating computational devices with the sort of perceptual abilities that people take for granted Blue eyes is being developed by the team of Poznan University of Technology& Microsoft. It makes use of the blue tooth technology developed by Ericsson.
PARTS OF A BLUE EYE SYSTEM :
The major parts in the Blue eye system are Data Acquisition Unit and Central System Unit. The tasks of the mobile Data Acquisition Unit are to maintain Bluetooth connections, to get information from the sensor and sending it over the wireless connection, to deliver the alarm messages sent from the Central System Unit to the operator and handle personalized ID cards. Central System Unit maintains the other side of the Blue tooth connection, buffers incoming sensor data, performs on-line data analysis, records the conclusions for further exploration and provides visualization interface.
THE HARDWARE:
Data Acquisition Unit
Data Acquisition Unit is a mobile part of the Blue eyes system. Its main task is to fetch the physiological data from the sensor and to send it to the central system to be processed. To accomplish the task the device must manage wireless Bluetooth connections (connection establishment, authentication and termination). Personal ID cards and PIN codes provide operator's authorization.
Figure Showing Jazz-multi Sensor
Communication with the operator is carried on using a simple 5-key keyboard, a small LCD display and a beeper. When an exceptional situation is detected the device uses them to notify the operator. Voice data is transferred using a small headset, interfaced to the DAU with standard mini-jack plugs.
The Data Acquisition Unit
The Data Acquisition unit comprises several hardware modules figure showing data
acquisition unit
¢ Atmel 89C52 microcontroller - system core
¢ Bluetooth module (based on ROK101008)
¢ HD44780 - small LCD display
¢ 24C16 - I2C EEPROM (on a removable ID card)
Block Diagram of Data Acquisition Unit:
¢ MC145483 “ 13bit PCM codec
¢ Jazz Multisensor interface
¢ beeper and LED indicators, 6 AA batteries and voltage level monitor
CENTRAL SYSTEM UNIT :
Central System Unit hardware is the second peer of the wireless connection. The box contains a Bluetooth module (based on ROK101008) and a PCM codec for voice data transmission. The module is interfaced to a PC using a parallel, serial and USB cable.
The audio data is accessible through standard mini-jack sockets over view of central system unit To program operator's personal ID cards we developed a simple programming device. The programmer is interfaced to a PC using serial and PS/2 (power source) ports. Inside, there is Atmel 89C2051 microcontroller, which handles UART transmission and I2C EEPROM (ID card) programming.
THE SOFTWARE:
Blue Eyes software's main task is to look after working operators' physiological condition. To assure instant reaction on the operators' condition change the software performs real time buffering of the incoming data, real-time physiological data analysis and alarm triggering.
The Blue Eyes software comprises several functional modules System core facilitates the
transfers flow between other system modules (e.g. transfers raw data from the Connection Manager to data analyzers, processed data from the data analyzers to GUI controls, other data analyzers, data logger etc.).
The System Core fundamental are single-producer-multi-consumer thread safe queues. Any number of consumers can register to receive the data supplied by a producer. Every single consumer can register at any number of producers, receiving therefore different types of data.
Naturally, every consumer may be a producer for other consumers. This approach enables high system scalability “ new data processing modules (i.e. filters, data analyzers and loggers) can be easily added by simply registering as a costumer
.
Connection Manager is responsible for managing the wireless communication between the mobile Data Acquisition Unit the central system. The Connection Manager handles:
¢ communication with the CSU hardware
¢ searching for new devices in the covered range
¢ establishing Bluetooth connections
¢ connection authentication
¢ incoming data buffering
¢ sending alerts
Data Analysis module performs the analysis of the raw sensor data in order to obtain information about the operatorâ„¢s physiological condition. The separately running Data Analysis module supervises each of the working operators.
The module consists of a number of smaller analyzers extracting different types of information. Each of the analyzers registers at the appropriate Operator Manager or another analyzer as a data consumer and, acting as a producer, provides the results of the analysis. The most important analyzers are:
¢ saccade detector - monitors eye movements in order to determine the level of operator's visual attention
¢ pulse rate analyzer - uses blood oxygenation signal to compute operator's pulse rate
¢ custom analyzers “ recognize other behaviors than those which are built-in the system. The new modules are created using C4.5 decision tree induction algorithm
Visualization module provides a user interface for the supervisors. It enables them to watch each of the working operatorâ„¢s physiological condition along with a preview of selected video source and related sound stream. All the incoming alarm messages are instantly signaled to the supervisor.
The Visualization module can be set in an off-line mode, where all the data is fetched from the database.
Watching all the recorded physiological parameters, alarms, video and audio data the supervisor is able to reconstruct the course of the selected operatorâ„¢s duty.
The physiological data is presented using a set of custom-built GUI controls:
¢ a pie-chart used to present a percentage of time the operator was actively acquiring the visual information
¢ A VU-meter showing the present value of a parameter time series displaying a history of selected parameters' value.
BLUE-EYES BENEFITS:
Prevention from dangerous incidents Minimization of ecological consequences financial loss a threat to a human life Blue Eyes system provides technical means for monitoring and recording human-operator's physiological condition. The key features of the system are:
¢ visual attention monitoring (eye motility analysis)
¢ physiological condition monitoring (pulse rate, blood oxygenation)
¢ operator's position detection (standing, lying)
¢ wireless data acquisition using Blue tooth technology
¢ real-time user-defined alarm triggering
¢ physiological data, operator's voice and overall view of the control room recording
¢ recorded data playback
Blue Eyes system can be applied in every working environment requiring permanent operator's attention:
¢ at power plant control rooms
¢ at captain bridges
¢ at flight control centers
CONCLUSION:
In future it is possible to create a computer which can interact with us as we interact each other with the use of blue eye technology. It seems to be a fiction, but it will be the life lead by BLUE EYES in the very near future. ordinary household devices -- such as televisions, refrigerators, and ovens -- may be able to do their jobs when we look at them and speak to them.


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12-04-2010, 11:26 PM
Post: #10
RE: Blue eyes (Download Full Report And Abstract)

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1.INTRODUCTION
Imagine yourself in a world where humans interact with computers. You are sitting in front of your personal computer that can listen, talk, or even scream aloud. It has the ability to gather information about you and interact with you through special techniques like facial recognition, speech recognition, etc. It can even understand your emotions at the touch of the mouse. It verifies your identity, feels your presents, and starts interacting with you .You ask the computer to dial to your friend at his office. It realizes the urgency of the situation through the mouse, dials your friend at his office, and establishes a connection.
Human cognition depends primarily on the ability to perceive, interpret, and integrate audio-visuals and sensoring information. Adding extraordinary perceptual abilities to computers would enable computers to work together with human beings as intimate partners. Researchers are attempting to add more capabilities to computers that will allow them to interact like humans, recognize human presents, talk, listen, or even guess their feelings.
The BLUEEYES technology aims at creating computational machines that have perceptual and sensory ability like those of human beings. It uses non-obtrusige sensing method, employing most modern video cameras and microphones to identify the user's actions through the use of imparted sensory abilities. The machine can understand what a user wants, where he is looking at, and even realize his physical or emotional states.
2. EMOTION AND COMPUTING
One goal of human computer interaction (HCI) is to make an adaptive, smart computer system. This type of project could possibly include gesture recognition, facial recognition, eye tracking, speech recognition, etc. Another non-invasive way to obtain information about a person is through touch. People use their computers to obtain, store and manipulate data using their computer. In order to start creating smart computers, the computer must start gaining information about the user. Our
proposed method for gaining user information through touch is via a computer input device, the mouse. From the physiological data obtained from the user, an emotional state may be determined which would then be related to the task the user is currently doing on the computer. Over a period of time, a user model will be built in order to gain a sense of the user's personality. The scope of the project is to have the computer adapt to the user in order to create a better working environment where the user is more productive. The first steps towards realizing this goal are described here.
Rosalind Picard (1997) describes why emotions are important to the computing community. There are two aspects of affective computing: giving the computer the ability to detect emotions and giving the computer the ability to express emotions. Not only are emotions crucial for rational decision making as Picard describes, but emotion detection is an important step to an adaptive computer system. An adaptive, smart computer system has been driving our efforts to detect a person's emotional state. An important element of incorporating emotion into computing is for productivity for a computer user. A study (Dryer & Horowitz, 1997) has shown that people with personalities that are similar or complement each other collaborate well. Dryer (1999) has also shown that people view their computer as having a personality. For these reasons, it is important to develop computers which can work well with its user. By matching a person's emotional state and the context of the expressed emotion, over a period of time the person's personality is being exhibited. Therefore, by giving the computer a longitudinal understanding of the emotional state of its user, the computer could adapt a working style which fits with its user's personality. The result of this collaboration could increase productivity for the user. One way of gaining information from a user non-intrusively is by video. Cameras have been used to detect a person's emotional state (Johnson, 1999). We have explored gaining information through touch. One obvious place to put sensors is on the mouse. Through observing normal computer usage (creating and editing documents and surfing the web), people spend approximately 1/3 of their total computer time touching their input device. Because of the incredible amount of time spent touching an input device, we will explore the possibility of detecting emotion through touch.
3. THEORIES AND TECHNOLOGIES
3.1 PAUL EKMAN'S FACIAL EXPRESSION
Based on Paul Ekman's facial expression work, we see a correlation between a person's emotional state and a person's physiological measurements. Selected works from Ekman and others on measuring facial behaviors describe Ekman's Facial Action Coding System (Ekman and Rosenberg, 1997). One of his experiments involved participants attached to devices to record certain measurements including pulse, galvanic skin response (GSR), temperature, somatic movement and blood pressure. He then recorded the measurements as the participants were instructed to mimic facial expressions which corresponded to the six basic emotions. He defined the six basic emotions as anger, fear, sadness, disgust, joy and surprise. From this work, Dryer (1993) determined how physiological measures could be used to distinguish various emotional states.
Six participants were trained to exhibit the facial expressions of the six basic emotions. While each participant exhibited these expressions, the physiological changes associated with affect were assessed. The measures taken were GSR, heart rate, skin temperature and general somatic activity (GSA). These data were then subject to two analyses. For the first analysis, a multidimensional scaling (MDS) procedure was used to determine the dimensionality of the data. This analysis suggested that the physiological similarities and dissimilarities of the six emotional states fit within a four dimensional model. For the second analysis, a discriminant function analysis was used to determine the mathematic functions that would distinguish the six emotional states. This analysis suggested that all four physiological variables made significant, nonredundant contributions to the functions that distinguish the six states. Moreover, these analyses indicate that these four physiological measures are sufficient to determine reliably a person's specific emotional state. Because of our need to incorporate these measurements into a small, non-intrusive form, we will explore taking these measurements from the hand. The amount of conductivity of the skin is best taken from the fingers. However, the other measures may not be as obvious or robust. We hypothesize that changes in the temperature of the finger are reliable for prediction of emotion. We also hypothesize the GSA can be measured by change in movement in the computer mouse. Our efforts to develop a robust pulse meter are not discussed here.
3.2 MANUAL AND GAZE INPUT CASCADED (MAGIC) POINTING
This work explores a new direction in utilizing eye gaze for computer input. Gaze tracking has long been considered as an alternative or potentially superior pointing method for computer input. We believe that many fundamental limitations exist with traditional gaze pointing. In particular, it is unnatural to overload a perceptual channel such as vision with a motor control task. We therefore propose an alternative approach, dubbed MAGIC (Manual And Gaze Input Cascaded) pointing. With such an approach, pointing appears to the user to be a manual task, used for fine manipulation and selection. However, a large portion of the cursor movement is eliminated by warping the cursor to the eye gaze area, which encompasses the target. Two specific MAGIC pointing techniques, one conservative and one liberal, were designed, analyzed, and implemented with an eye tracker we developed. They were then tested in a pilot study. This early stage exploration showed that the MAGIC pointing techniques might offer many advantages, including reduced physical effort and fatigue as compared to traditional manual pointing, greater accuracy and naturalness than traditional gaze pointing, and possibly faster speed than manual pointing. The pros and cons of the two techniques are discussed in light of both performance data and subjective reports.
3.2.1.IMPLIMENTATION
The MAGIC pointing program takes data from both the manual input device (of any type, such as a mouse) and the eye tracking system running either on the same machine or on another machine connected via serial port. Raw data from an eye tracker can not be directly used for gaze-based interaction, due to noise from image processing, eye movement jitters, and samples taken during saccade (ballistic eye movement) periods. We experimented with various filtering techniques and found the most effective filter in our case is similar to that described. The goal of filter design in general is to make the best compromise between preserving signal bandwidth and eliminating unwanted noise. In the case of eye tracking, as Jacob argued, eye information relevant to interaction lies in the fixations. The key is to select fixation points with minimal delay. Samples collected during a saccade are unwanted and should be avoided. In designing our algorithm for picking points of fixation, we considered our tracking system speed (30 Hz), and that the MAGIC pointing techniques utilize gaze information only once for each new target, probably immediately after a saccade. Our filtering algorithm was designed to pick a fixation with minimum delay by means of selecting two adjacent points over two samples.
3.3 ARTIFICIAL INTELLIGENT SPEECH RECOGNITION
It is important to consider the environment in which the speech recognition system has to work. The grammar used by the speaker and accepted by the system, noise level, noise type, position of the microphone, and speed and manner of the user's speech are some factors that may affect the quality of speech recognition .When you dial the telephone number of a big company, you are likely to hear the sonorous voice of a cultured lady who responds to your call with great courtesy saying "Welcome to company X. Please give me the extension number you want". You pronounce the extension number, your name, and the name of person you want to contact. If the called person accepts the call, the connection is given quickly. This is artificial intelligence where an automatic call-handling system is used without employing any telephone operator.
3.3.1THETECHNOLOGY
Artificial intelligence (AI) involves two basic ideas. First, it involves studying the thought processes of human beings. Second, it deals with representing those processes via machines (like computers, robots, etc). AI is behavior of a machine, which, if performed by a human being, would be called intelligent. It makes machines smarter and more useful, and is less expensive than natural intelligence. Natural language processing (NLP) refers to artificial intelligence methods of communicating with a computer in a natural language like English. The main objective of a NLP program is to understand input and initiate action. The input words are scanned and matched against internally stored known words. Identification of a key word causes some action to be taken. In this way, one can communicate with the computer in one's language. No special commands or computer language are required. There is no need to enter programs in a special language forcreating software.
3.3.2 SPEECH RECOGNITION
The user speaks to the computer through a microphone, which, in used; a simple system may contain a minimum of three filters. The more the number of filters used, the higher the probability of accurate recognition. Presently, switched capacitor digital filters are used because these can be custom-built in integrated circuit form. These are smaller and
cheaper than active filters using operational amplifiers. The filter output is then fed to the ADC to translate the analogue signal into digital word. The ADC samples the filter outputs many times a second. Each sample represents different amplitude of the signal .Evenly spaced vertical lines represent the amplitude of the audio filter output at the instant of sampling. Each value is then converted to a binary number proportional to the amplitude of the sample. A central processor unit (CPU) controls the input circuits that are fed by the ADCS. A large RAM (random access memory) stores all the digital values in a buffer area. This digital information, representing the spoken word, is now accessed by the CPU to process it further. The normal speech has a frequency range of 200 Hz to 7 kHz. Recognizing a telephone call is more difficult as it has bandwidth limitation of 300 Hz to3.3 kHz.As explained earlier, the spoken words are processed by the filters and ADCs. The binary representation of each of these words becomes a template or standard, against which the future words are compared. These templates are stored in the memory. Once the storing process is completed, the system can go into its active mode and is capable of identifying spoken words. As each word is spoken, it is converted into binary equivalent and stored in RAM. The computer then starts searching and compares the binary input pattern with the templates. t is to be noted that even if the same speaker talks the same text, there are always slight variations in amplitude or loudness of the signal, pitch, frequency difference, time gap, etc. Due to this reason, there is never a perfect match between the template and binary input word. The pattern matching process therefore uses statistical techniques and is designed to look for the best fit.
The values of binary input words are subtracted from the corresponding values in the templates. If both the values are same, the difference is zero and there is perfect match. If not, the subtraction produces some difference or error. The smaller the error, the better the match. When the best match occurs, the word is identified and displayed on the screen or used in some other manner. The search process takes a considerable amount of time, as the CPU has to make many comparisons before recognition occurs. This necessitates use of very high-speed processors. A large RAM is also required as even though a spoken word may last only a few hundred milliseconds, but the same is translated into many thousands of digital words. It is important to note that alignment of words and templates are to be matched correctly in time, before computing the similarity score. This process, termed as dynamic time warping, recognizes that different speakers pronounce the same words at different speeds as well as elongate different parts of the same word. This is important for the speaker-independent recognizers.
3.4 THE SIMPLEUSER INTERST TRACKER (SUITOR)
Computers would have been much more powerful, had they gained perceptual and sensory abilities of the living beings on the earth. What needs to be developed is an intimate relationship between the computer and the humans. And the Simple User Interest Tracker (SUITOR) is a revolutionary approach in this direction.
By observing the Webpage a netizen is browsing, the SUITOR can help by fetching more information at his desktop. By simply noticing where the user's eyes focus on the computer screen, the SUITOR can be more precise in determining his topic of interest. It can even deliver relevant information to a handheld device. The success lies in how much the suitor can be intimate to the user. IBM's BlueEyes research project began with a simple question, according to Myron Flickner, a manager in Almaden's USER group: Can we exploit nonverbal cues to create more effective user interfaces? One such cue is gaze”the direction in which a person is looking. Flickner and his colleagues have created some new techniques for tracking a person's eyes and have incorporated this gaze-tracking technology into two prototypes. One, called SUITOR (Simple User Interest Tracker), fills a scrolling ticker on a computer screen with information related to the user's current task. SUITOR knows where you are looking, what applications you are running, and what Web pages you may be browsing. "If I'm reading a Web page about IBM, for instance," says Paul Maglio, the Almaden cognitive scientist who invented SUITOR, "the system presents the latest stock price or business news stories that could affect IBM. If I read the headline off the ticker, it pops up the story in a browser window. If I start to read the story, it adds related stories to the ticker. That's the whole idea of an attentive system”one that attends to what you are doing, typing, reading, so that it can attend to your information needs."
4. POZNAN BLUEEYES
The system developed by Poznan University of Technology is intended to be the complex solution for monitoring and recording the operator's conscious brain involvement as well as his physiological condition. This required designing a Personal Area Network linking all the operators and the supervising system. As the operator using his sight and hearing senses the state of the controlled system, the supervising system will look after his physiological condition.
4.1 SYSTEM OVERVIEW
BLUEEYES system provides technical means for monitoring and recording the operator's basic physiological parameters. The most important parameter is saccadic activity1, which enables the system to monitor the status of the operator's visual attention along with head acceleration, which accompanies large displacement of the visual axis (saccades larger than 15 degrees). Complex industrial environment can create a danger of exposing the operator to toxic substances, which can affect his cardiac, circulatory and pulmonary systems. Thus, on the grounds of lethysmographic signal taken from the forehead skin surface, the system computes heart beat rate and blood oxygenation. The BLUEEYES system checks above parameters against abnormal (e.g. a low level of blood oxygenation or a high pulse rate) or undesirable (e.g. a longer period of lowered visual attention) values and triggers user-defined alarms when necessary. Quite often in an emergency situation operators speak to themselves expressing their surprise or stating verbally the problem. Therefore, the operator's voice, physiological parameters and an overall view of the operating room are recorded. This helps to reconstruct the course of operators' work and provides data for long-term analysis. This system consists of a mobile measuring device and a central analytical system. The mobile device is integrated with Bluetooth module providing wireless interface between sensors worn by the operator and the central unit. ID cards assigned to each of the operators and adequate user profiles on the central unit side provide necessary data personalization so different people can use a single mobile device.
4.2 TOOLS DEVELOPED
In creating the hardware part of the DAU we built a development board, which enabled us to mount, connect and test various peripheral devices cooperating with the microcontroller.During the implementation of the DAU we needed a piece of software to establish and test Bluetooth connections. We therefore created a tool called BlueDentist. The tool provides support for controlling the currently connected Bluetooth device. Its functions are: local device management (resetting, reading local BDADDR, putting in Inquiry/Page and Inquiry/Page scan modes, reading the list of locally supported features and setting UART speed) and connection management (receiving and displaying Inquiry scan results, establishing ACL links,
adding SCO connections, performing link authorization procedure, sending test data packets and disconnecting).To test the possibilities and performance of the remaining parts of the Project Kit (computer, camera and database software) we created BlueCapture The tool supports capturing video data from various sources (USB web-cam, industrial camera) and storing the data in the MS SQL Server database. Additionally, the application performs sound recording. After filtering and removing insignificant fragments (i.e. silence) the audio data is stored in the database. Finally, the program plays the recorded audiovisual stream. We used the software to measure database system performance and to optimize some of the SQL queries (e.g. we replaced correlated SQL queries with cursor operations). Since all the components of the application have been tested thoroughly they were reused in the final software, which additionally reduced testing time.We also created a simple tool for recording Jazz Multisensor measurements. The program reads the data using a parallel port and writes it to a file.To program the operator's personal ID card we use a standard parallel port, as the EPROMs and the port are both TTL-compliant. A simple dialog-based application helps to accomplish the task.
5. APPLICATIONS
1. Researchers from IBM Center in San Jose, CA, report that a number of large retailers have implemented surveillance systems that record and interpret customer movements, using software from Almaden's BlueEyes research project. BlueEyes software makes sense of what the cameras see to answer key questions for retailers, including, How many shoppers ignored a promotion? How many stopped? How long did they stay? Did their faces register boredom or delight? How many reached for the item and put it in their shopping carts? BlueEyes works by tracking pupil, eyebrow and mouth movement. When monitoring pupils, the system uses a camera and two infrared light sources placed inside the product display. One light source is aligned with the camera's focus; the other is slightly off axis. When the eye looks into the camera-aligned light, the pupil appears bright to the sensor, and the software registers the customer's attention. This is way it captures the person's income and buying preferences. BlueEyes is actively been incorporated in some of the leading retail outlets.
2. Blue Eyes can be applied in the automobile industry. By simply touching a computer input device such as a mouse, the computer system is designed to be able to determine a person's emotional state. For cars, it could be useful to help with critical decisions like: "I know you want to get into the
fast lane, but I'm afraid I can't do that. Your too upset right now" and therefore assist in driving safely.
3. We could see its use in video games where, it could give individual challenges to customers playing video games. Typically targeting commercial business.The integration of children's toys, technologies and computers is enabling new play experiences that were not commercially feasible until recently. The Intel Play QX3 Computer Microscope, the Me2Cam with Fun Fair, and the Computer Sound Morpher are commercially available smart toy products developed by the Intel Smart Toy Lab in. One theme that is common across these PC-connected toys is that users interact with them using a combination of visual, audible and tactile input & output modalities. The presentation will provide an overview of the interaction design of these products and pose some unique challenges faced by designers and engineers of such experiences targeted at novice computer users, namely young children.
4. The familiar and useful come from things we recognize. Many of our favorite things' appearance communicate their use; they show the change in their value though patina. As technologists we are now poised to imagine a world where computing objects communicate with us in-situ; where we are. We use our looks, feelings, and actions to give the computer the experience it needs to work with us. Keyboards and mice will not continue to dominate computer user interfaces. Keyboard input will be replaced in large measure by systems that know what we want and require less explicit communication. Sensors are gaining fidelity and ubiquity to record presence and actions; sensors will notice when we enter a space, sit down, lie down, pump iron, etc. Pervasive infrastructure is recording it. This talk will cover projects from the Context Aware Computing Group at MIT Media Lab.
5. Current interfaces between computers and humans can present information vividly, but have no sense of whether that information is ever viewed or understood. In contrast, new real-time computer vision techniques for perceiving people allows us to create "Face-responsive Displays" and "Perceptive Environments", which can sense and respond to users that are viewing them. Using stereo-vision techniques, we are able to detect, track, and identify users robustly and in real time. This information can make spoken language interface more robust, by selecting the acoustic information from a visually-localized source. Environments can become aware of how many people are present, what activity is occurring, and therefore what display or messaging modalities are most appropriate to use in the current
situation. The results of our research will allow the interface between computers and human users to become more natural and intuitive.
6. CONCLUSION
The nineties witnessed quantum leaps interface designing for improved man machine interactions. The BLUE EYES technology ensures a convenient way of simplifying the life by providing more delicate and user friendly facilities in computing devices. Now that we have proven the method, the next step is to improve the hardware. Instead of using cumbersome modules to gather information about the user, it will be better to use smaller and less intrusive units. The day is not far when this technology will push its way into your house hold, making you more lazy. It may even reach your hand held mobile device. Any way this is only a technological forecast.

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25-04-2010, 12:09 PM
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RE: Blue eyes (Download Full Report And Abstract)

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BLUE EYE TECHNOLOGY


What is blue eye
technology


The BLUE EYES technology aims at creating computational machines that have perceptual and sensory ability like those of human beings.



How can we make

computers see and feel
Blue eyes:
Identifies userâ„¢s actions using sensing technology.
Extracts key information.
Analyzes the information.
Finally, determines the userâ„¢s physical, emotional and informational state.


Designing

The Blue Eyes uses:
A personal area network for
linking all the operators and the
supervising system
Two major units
- DAU (data acquisition unit )
- CSU (central system unit )




DATA ACQUISITION UNIT:

Gets information from the sensor.
Sends information over the wireless connection.
Delivers the alarm messages sent from the Central System Unit (CSU) to the operator.



Jazz Multisensor:

Itâ„¢s an eye movement sensor, to provide necessary physiological data in Data Acquisition Unit (DAU).

It supplies raw digital data regarding eye position, the level of blood oxygenation acceleration along horizontal and vertical axes and ambient light intensity.
Eye movement can be measured using direct infrared oculographic transducers.
Jazz Multisensor




CENTRAL SYSTEM UNIT:

Buffers incoming sensor data.
Performs on-line data analysis.
Records the conclusion for further exploration.
Provides visualization interface.


Applications:

Blue Eyes system can be applied in every working environment requiring permanent operator's attention.
At power plant control rooms.
At captain bridges.
At flight control centers.
Professional drivers.


IBM Research:

BLUE EYE EMOTIONAL MOUSE- sensors in the mouse ,sense the physiological attributes which are correlated to emotions using correlation model by simply touching the mouse the computer will be able to determine a personâ„¢s emotional state.
BLUE EYE enabled TELEVISION “ could become active when the user makes an eye contact



Conclusion:

In the near future ,ordinary household devices- such as television , refrigerators, ovens may be able to do their jobs when we look at them and speak to them.
It avoids potential threats resulting from human errors, such as weariness, oversight, tiredness.

Thank you

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08-06-2010, 04:07 PM
Post: #12
Smile RE: Blue eyes (Download Full Report And Abstract)
Nice topic for seminar
28-06-2010, 06:07 PM
Post: #13
RE: Blue eyes (Download Full Report And Abstract)
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08-07-2010, 10:20 PM
Post: #14
RE: Blue eyes (Download Full Report And Abstract)
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15-07-2010, 11:27 AM
Post: #15
RE: Blue eyes (Download Full Report And Abstract)
Thank you very very much.
29-07-2010, 09:28 AM
Post: #16
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12-08-2010, 08:47 AM
Post: #17
RE: Blue eyes (Download Full Report And Abstract)
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12-08-2010, 11:17 AM
Post: #18
RE: Blue eyes (Download Full Report And Abstract)
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12-08-2010, 11:43 AM
Post: #19
RE: Blue eyes (Download Full Report And Abstract)
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12-08-2010, 07:53 PM
Post: #20
RE: Blue eyes (Download Full Report And Abstract)
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12-08-2010, 09:17 PM
Post: #21
RE: Blue eyes (Download Full Report And Abstract)
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25-09-2010, 11:18 AM
Post: #22
RE: Blue eyes (Download Full Report And Abstract)

.doc  090420102003.doc (Size: 596 KB / Downloads: 180)

Blue eyes

ABSTRACT

Is it possible to create a computer which can interact with us as we interact each other? For example imagine in a fine morning you walk on to your computer room and switch on your computer, and then it tells you “Hey friend, good morning you seem to be a bad mood today. And then it opens your mail box and shows you some of the mails and tries to cheer you. It seems to be a fiction, but it will be the life lead by “BLUE EYES” in the very near future.
The basic idea behind this technology is to give the computer the human power. We all have some perceptual abilities. That is we can understand each other’s feelings. For example we can understand ones emotional state by analyzing his facial expression. If we add these perceptual abilities of human to computers would enable computers to work together with human beings as intimate partners. The “BLUE EYES” technology aims at creating computational machines that have perceptual and sensory ability like those of human beings.
27-09-2010, 01:25 PM
Post: #23
Tongue Blue eyes (Download ppt)

.ppt  BLUE EYE TECHNOLOGY.ppt (Size: 635 KB / Downloads: 219)
This article is presented by:
Shreya Pergade
Deeksha k.
VI th semester
Computer science & Eng


INTRODUCTION

Blue Eye is the technology to make computer to sense and understand human behavior and feelings and respond in the proper way.

System overview

Bluetooth technology provides means for creating a Personal Area Network linking the operators and the central system.


DAU-features

Lightweight
Runs on batteries - low power consumption
Easy to use - does not disturb the operator working
ID cards for operator authorization
Voice transmission using hardware PCM codec

30-09-2010, 01:07 PM
Post: #24
RE: Blue eyes (Download Full Report And Abstract),blue eye technology seminar repor

.ppt  408.ppt (Size: 1.4 MB / Downloads: 148)

Blue Eye Technology


By:
ARUN DIXIT




CONTENTS

Motivation
What is BlueEye technology ?
What is BlueEyes ?
System designing
System overview
DAU
CSU
IBM research
Conclusion
30-09-2010, 04:58 PM
Post: #25
Music RE: Blue eyes (Download Full Report And Abstract)

.doc  blue-eyes.doc (Size: 323.5 KB / Downloads: 143)
BLUE EYES technology

1 INTRODUCTION


Imagine yourself in a world where humans interact with computers. You are sitting in front of your personal computer that can listen, talk, or even scream aloud. It has the ability to gather information about you and interact with you through special techniques like facial recognition, speech recognition, etc. It can even understand your emotions at the touch of the mouse. It verifies your identity, feels your presents, and starts interacting with you .You ask the computer to dial to your friend at his office. It realizes the urgency of the situation through the mouse, dials your friend at his office, and establishes a connection.
Human cognition depends primarily on the ability to perceive, interpret, and integrate audio-visuals and sensoring information. Adding extraordinary perceptual abilities to computers would enable computers to work together with human beings as intimate partners. Researchers are attempting to add more capabilities to computers that will allow them to interact like humans, recognize human presents, talk, listen, or even guess their feelings.
The BLUE EYES technology aims at creating computational machines that have perceptual and sensory ability like those of human beings. It uses non-obtrusige sensing method, employing most modern video cameras and microphones to identifies the users actions through the use of imparted sensory abilities . The machine can understand what a user wants, where he is looking at, and even realize his physical or emotional states.
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