THESIS
2014
iii leaves, iii-xi, 73 pages : illustrations ; 30 cm
Abstract
Eye gaze direction is a powerful cue for users' intent. However, it is difficult to interpret in natural
situations, since gaze serves multiple purposes. Here, we demonstrate that by modeling different
gaze behaviors and the transitions between them during a cursor guidance task that includes an obstacle avoidance constraint using a Hidden Markov Model, we can infer the users' goal out of a
field of 49 possibilities. Users are not given any specific instructions regarding their gaze, and
typically spend only a small fraction of the time looking at their intended target. Nonetheless, our
experimental results indicate that the hidden Markov model for gaze enables reliable user
independent identification of the target of the cursor movement. The accuracy with which the
target region...[
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Eye gaze direction is a powerful cue for users' intent. However, it is difficult to interpret in natural
situations, since gaze serves multiple purposes. Here, we demonstrate that by modeling different
gaze behaviors and the transitions between them during a cursor guidance task that includes an obstacle avoidance constraint using a Hidden Markov Model, we can infer the users' goal out of a
field of 49 possibilities. Users are not given any specific instructions regarding their gaze, and
typically spend only a small fraction of the time looking at their intended target. Nonetheless, our
experimental results indicate that the hidden Markov model for gaze enables reliable user
independent identification of the target of the cursor movement. The accuracy with which the
target region is identified increases over time, eventually surpassing 80%. We applied this model
to a human machine interface for a robotic arm reaching task. We show that performance in target
identification reaches 80%. In comparison with a rule based algorithm, which makes use of less
history information, the HMM based inference improves performance significantly, suggesting
that proper modeling of the entire gaze trajectory is critical in analyzing gaze.
Keywords - Gaze, Eye Tracking, Hidden Markov model, Intent
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