Estimation of situation awareness score and performance using eye and head gaze for human-robot collaboration

Lucas Paletta, Amir Dini, Cornelia Murko, Saeed Yahyanejad, Ursula Augsdörfer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Human attention processes play a major role in the optimization of human-robot collaboration (HRC) [Huang et al. 2015]. We describe a novel methodology to measure and predict situation awareness from eye and head gaze features in real-time. The awareness about scene objects of interest was described by 3D gaze analysis using data from eye tracking glasses and a precise optical tracking system. A probabilistic framework of uncertainty considers coping with measurement errors in eye and position estimation. Comprehensive experiments on HRC were conducted with typical tasks including handover in a lab based prototypical manufacturing environment. The gaze features highly correlate with scores of standardized questionnaires of situation awareness (SART [Taylor 1990], SAGAT [Endsley 2000]) and predict performance in the HRC task. This will open new opportunities for human factors based optimization in HRC applications.
Original languageEnglish
Title of host publicationProceedings of the 11th ACM Symposium on Eye Tracking Research & Applications
Place of PublicationNew York
Pages1-3
Volume61
Publication statusPublished - 25 Jun 2019

Keywords

  • dual task
  • human-robot collaboration
  • situation awareness

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