Friday, February 13

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 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.

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. 

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.



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.


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. 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.

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 cannot 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. The goal of filter design in general is to make the best compromise between preserving signal bandwidth and eliminating unwanted noise. 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.

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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