Eye-tracking for performance evaluation and workload estimation in space telerobotic training

Guo, Y., Freer, D., Deligianni, F. and Yang, G.-Z. (2021) Eye-tracking for performance evaluation and workload estimation in space telerobotic training. IEEE Transactions on Human-Machine Systems, (doi: 10.1109/THMS.2021.3107519) (Early Online Publication)

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Abstract

Monitoring the mental workload of operators is of paramount importance in space telerobotic training and other teleoperation tasks. Instead of the estimation of task-specific workload, this article aims at investigating the impact of two significant confounding factors (time-pressure and latency) on space teleoperation and explored the use of eye-tracking technology for factor-induced mental workload estimation and performance evaluation. Ten subjects teleoperated a Canadarm2 robot to complete a complex on-orbit assembly task in our photo-realistic training simulator while wearing a head-mounted eye-tracker. To understand how time-pressure and latency influence eye-tracking features works, we first performed the statistical analysis on various features with respect to a single factor and across multiple groups. Next, eye-tracking features extracted from segment data and trial data is used to identify the mental workload induced by confounding factors, which can be used for developing personalized training programs and guaranteeing safe teleoperation. Furthermore, to improve the recognition performance using segment data, we propose the activity ratio and time ratio to characterize the informative segments. Finally, the relationship between simulator-defined performance measures and eye-tracking features is examined. Results show that fixation duration, saccade frequency and duration, pupil diameter, and index of pupillary activity are significant features that can be used in both factor-induced mental workload estimation and task performance evaluation.

Item Type:Articles
Additional Information:This work was supported by Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/R026092/1.
Status:Early Online Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Deligianni, Dr Fani
Authors: Guo, Y., Freer, D., Deligianni, F., and Yang, G.-Z.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:IEEE Transactions on Human-Machine Systems
Publisher:IEEE
ISSN:2168-2291
ISSN (Online):2168-2305
Published Online:13 September 2021
Copyright Holders:Copyright © 2021 IEEE
First Published:First published in IEEE Transactions on Human-Machine Systems 2021
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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