Mental workload characteristics of manipulator teleoperators with different spatial cognitive abilities

• Published in: International journal of advanced robotic systems Vol. 16; no. 6; p. 172988141988804
• Main Authors: Shao, Shuyu, Zhou, Qianxiang, Liu, Zhongqi
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.11.2019; Sage Publications Ltd; SAGE Publishing
• Subjects: Cognitive ability; Electroencephalography; Teleoperators; Time lag; Topographic maps; Wavelet; Workload; Workloads; teleoperation; human–machine interaction; mental workload; EEG; Spatial cognitive ability
• ISSN: 1729-8806; 1729-8814; 1729-8814
• DOI: 10.1177/1729881419888042

The main research on manipulator teleoperation includes robust of high-degree of freedom manipulators, sensor measurement accuracy, time delay, and mechanical structure design. Increased mental capacity requirements for complex assignments result in an increased mental workload. Spatial cognitive ability was considered to be the key factor affecting teleoperation performance. To accomplish this, we had 50 participants performed teleoperation while recorded their electroencephalogram. Electroencephalogram data of each task were divided into two periods, which correspond to the observation and large-scale transfer stages of teleoperation, respectively (period 1) and adjust the attitude of the manipulator to approach and align with the target stage (period 2). Brain topographic maps of period 1 (period 1 wavelet packet energy minus resting state wavelet packet energy) and period 2 (period 2 wavelet packet energy minus resting state wavelet packet energy) show that the frontal, central, and occipital regions are the main working areas of low spatial cognitive operators in period 1, while the frontal, central, and occipital regions are the main working areas of high spatial cognitive operators in period 1. The main changes in period 2 were frontal, central, parietal, and occipital regions. This study has implication for the analysis of electroencephalogram signal characteristics of mental workload in different populations to improve operators’ well-being and safety at teleoperation work.