CONTEN TS • Introduction • Emotion mouse • Emotion and computing • Theory • Result • Manual and gaze input cascaded (magic) pointing • Eye tracker • Implementing magic pointing • Artificial intelligent speech recognition • Application • The simple user interface tracker • conclusion 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. The BLUE EYES technology aims at creating computational machines that have perceptual and sensory ability like those of human beings. Employing most modern video cameras and microphones to identifies the users actions through the use of imparted sensory abilities . The machin an understand what a user wants, where he is looking at, and even realize his physical or emotional states. 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.
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. 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. 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 nonintrusively is by video. Cameras have been used to detect a person’s emotional state. We have explored gaining information through touch. One obvious place to put sensors is on the mouse. 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. 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. Result: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. 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 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. Manual and gaze (magic) pointing:- input cascaded 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 undamental 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 and pointing liberal, techniques, were one conservative one designed, nalyzed, 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. 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 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 , 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. 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. We have designed two MAGIC pointing techniques, one liberal and the other conservative in terms of target identification and cursor placement. 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 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. 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.
Eye tracking data can be acquired simultaneously with MRI scanning using a system that illuminates the left eye of a subject with an infrared (IR) source, acquires a video image of that eye, locates the corneal reflection (CR) of the IR source, and in real time calculates/displays/records the gaze direction and pupil diameter. Once the pupil has been detected, the corneal reflection 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.
An initial calibration procedure, similar to that required by commercial eye trackers. Implementing 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. 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. Our filtering algorithm was designed to pick a fixation with minimum delay by means of selecting two adjacent points over two samples.
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. Application:One of the main benefits of speech recognition system is that and it The lets user user can do other works on still simultaneously. observation concentrate and manual operations, ontrol 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, dictating system CT scans to and simultaneously The conclusions to a speech recognition connected word processors. adiologist 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. The simple user interface tracker: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. 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. 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. THANK YOU Seminar report on Submitted to:Submitted by:Prof. S. K. prabhash pratap singh E. C. 3rdyear ( 0904EC061088) Ranjeet