Physiological Measures for Human Performance Analysis in Human-Robot Teamwork: Case of Tele-Exploration

IEEE Access

Amirhossein H. Memar¹  Ehsan T. Esfahani¹ 

[1] Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA;

关键词: Human-robot interaction;    human performance;    individual differences;    mental workload;    physiological measures;    situation awareness;   

DOI  :  10.1109/ACCESS.2018.2790838

来源: DOAJ

【 摘 要 】

Continuous monitoring of mental workload and situation awareness in operational environments is useful for understanding and prediction of human performance. Such information can be used to develop real-time adaptive systems to enhance human performance. In this paper, we investigate the use of work load- and attention-related physiological measures to predict operator performance and situation awareness in the context of tele-exploration with a small team of robots. A user study is conducted based on a simulated scenario involving visual scanning and manual control tasks with varying levels of task-load. Brain activity and eye movements of the participants are monitored across the experimental tasks using electroencephalogram and eye tracker sensors. The performances of the subjects are evaluated in terms of target detection and situation awareness (primary metrics) as well as reaction time and false detection (secondary metrics). Moreover, individual differences in two specific visual skills, visual search (VS) and multi-object tracking (MOT) are considered as between-subject factors in the experimental design. The main effects of task type and individual differences reveal that VS and MOT skill have significant effects on target detection and situation awareness, respectively. The correlations of physiological measures with the task performance and situation awareness are analyzed. The results suggest that brain-based features (mental workload and distraction) which represent the covert aspect of attention are better suited to predict the secondary performance metrics. On the other hand, glance-based features which represent the overt aspect of attention are shown to be the best predictors of the primary performance metrics.

【 授权许可】

Unknown