Utilizing Gaze Behavior for Inferring Task Transitions Using Abstract Hidden Markov Models

  • Daniel Fernando Tello Gamarra

Abstract

We demonstrate an improved method for utilizing observed gaze behavior and show that it is useful in inferring hand movement intent during goal directed tasks. The task dynamics and the relationship between hand and gaze behavior are learned using an Abstract Hidden Markov Model (AHMM). We show that the predicted hand movement transitions occur consistently earlier in AHMM models with gaze than those models that do not include gaze observations.

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Published
2016-12-18
How to Cite
Tello Gamarra, D. (2016). Utilizing Gaze Behavior for Inferring Task Transitions Using Abstract Hidden Markov Models. Inteligencia Artificial, 19(58), 1-16. https://doi.org/10.4114/intartif.vol19iss58pp1-16