Utilizing gaze behavior for inferring task transitions using abstract hidden Markov models

Utilizing gaze behavior for inferring task transitions using abstract hidden Markov models

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...

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Bibliographic Details
Main Authors: Pinpin, Lord Kenneth M., Gamarra, Daniel Fernando Tello, Johansson, Roland S., Laschi, Cecilia, Dario, Paolo
Format: text
Published: Animo Repository 2012
Subjects:
Bionics
Gaze
Robots—Control systems
Hidden Markov models
Artificial intelligence
Artificial Intelligence and Robotics
Online Access:https://animorepository.dlsu.edu.ph/faculty_research/12798
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Institution: De La Salle University
Description
Summary: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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