Wednesday, March 12, 2008

Eye Gaze Interaction with Expanding Targets (Minotas, Spakov, MacKenzie, 2004)

Continuing on the topic of expanding areas this paper presents an approach where the expansion of the target area is invisible.The authors introduce their algorithm called "Grab-and-hold" which aims at stablizing the gaze data and performs a two part experiment to evaluate it.

"Recent evidence on the performance benefits of expanding targets during manual pointing raises a provocative question: Can a similar effect be expected for eye gaze interaction? We present two experiments to examine the benefits of target expansion during an eye-controlled selection task. The second experiment also tested the efficiency of a “grab-and-hold algorithm” to counteract inherent eye jitter. Results confirm the benefits of target expansion both in pointing speed and accuracy. Additionally, the grab-and-hold algorithm affords a dramatic 57% reduction in error rates overall. The reduction is as much as 68% for targets subtending 0.35 degrees of visual angle. However, there is a cost which surfaces as a slight increase in movement time (10%). These findings indicate that target expansion coupled with additional measures to accommodate eye jitter has the potential to make eye gaze a more suitable input modality." (Paper available here)

Their "Grab-and-hold" algorithm that puts some more intelligent processing of the gaze data. "Upon appearance of the target, there is a settle-down period of 200 ms during which the gaze is expected to land in the target area and stay there. Then, the algorithm filters the gaze points until the first sample inside the expanded target area is logged. When this occurs, the target is highlighted and the selection timer triggered. The selection timer counts down a specified dwell time (DT) interval. "

While reading this paper I came to think about an important question concerning filtering of gaze data. The delay that comes from collecting the samples used for the algorithm processing causes a delay in the interaction. For example, if I were to sample 50 gaze positions and then average these to reduce the jitter it would result in a one second delay on a system that captures 50 images per second (50Hz) As seen in other papers as well there is a speed-accuracy trade off to make. What is more important, a lower error rate or a more responsive system?

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