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

Authors

Lord Kenneth M. Pinpin, De La Salle University, Manila
Daniel Fernando Tello Gamarra
Roland S. Johansson
Cecilia Laschi
Paolo Dario

College

Gokongwei College of Engineering

Department/Unit

Manufacturing Engineering and Management

Document Type

Archival Material/Manuscript

Publication Date

2012

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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Recommended Citation

Pinpin, L. M., Gamarra, D. T., Johansson, R. S., Laschi, C., & Dario, P. (2012). Utilizing gaze behavior for inferring task transitions using abstract hidden Markov models. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/12798

Disciplines

Artificial Intelligence and Robotics

Note

Undated; Publication/creation date supplied

Keywords

Bionics; Gaze; Robots—Control systems; Hidden Markov models; Artificial intelligence

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