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

Autor:	Saeed Yahyanejad, Lucas Paletta, Amir Dini, Cornelia Murko, Ursula H. Augsdörfer
Rok vydání:	2019
Předmět:	Optical tracking Situation awareness Handover Human–computer interaction Computer science Eye tracking Probabilistic framework Gaze Human–robot interaction
Zdroj:	ETRA
DOI:	10.1145/3314111.3322504
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::efad664595c81b4b55d9853ef2c9f910 https://doi.org/10.1145/3314111.3322504 Zobrazit plný text záznamu