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Research of HRV as a Measure of Mental Workload in Human and Dual-Arm Robot Interaction
Shao SL(邵士亮)1,2; Wang T(王挺)1,2; Wang YL(王勇亮)1,2,3; Su Y(苏赟)1,2,3; Song CH(宋纯贺)1,2; Yao C(姚辰)1,2
Department机器人学研究室
Source PublicationELECTRONICS
ISSN2079-9292
2020
Volume9Issue:12Pages:1-17
Indexed BySCI ; SSCI
WOS IDWOS:000602075100001
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [U20A20201] ; Doctoral Scientific Research Foundation of Liaoning Province [2020-BS-025] ; LiaoNing Revitalization Talents Program [XLYC1807018] ; National key research and development program of China [2016YFE0206200]
Keywordhuman-robot interaction mental workload heart rate variability machine learning
Abstract

Robots instead of humans work in unstructured environments, expanding the scope of human work. The interactions between humans and robots are indirect through operating terminals. The mental workloads of human increase with the lack of direct perception to the real scenes. Thus, mental workload assessment is important, which could effectively avoid serious accidents caused by mental overloading. In this paper, the operating object is a dual-arm robot. The classification of operator's mental workload is studied by using the heart rate variability (HRV) signal. First, two kinds of electrocardiogram (ECG) signals are collected from six subjects who performed tasks or maintained a relaxed state. Then, HRV data is obtained from ECG signals and 20 kinds of HRV features are extracted. Last, six different classifications are used for mental workload classification. Using each subject's HRV signal to train the model, the subject's mental workload is classified. Average classification accuracy of 98.77% is obtained using the K-Nearest Neighbor (KNN) method. By using the HRV signal of five subjects for training and that of one subject for testing with the Gentle Boost (GB) method, the highest average classification accuracy (80.56%) is obtained. This study has implications for the analysis of HRV signals characteristic of mental workload in different subjects, which could improve operators' well-being and safety in the human-robot interaction process.

Language英语
WOS SubjectEngineering, Electrical & Electronic
WOS KeywordHEART-RATE-VARIABILITY ; CLASSIFICATION ; SLEEP
WOS Research AreaEngineering
Funding ProjectNational Natural Science Foundation of China[U20A20201] ; Doctoral Scientific Research Foundation of Liaoning Province[2020-BS-025] ; LiaoNing Revitalization Talents Program[XLYC1807018] ; National key research and development program of China[2016YFE0206200]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28144
Collection机器人学研究室
Corresponding AuthorShao SL(邵士亮)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and IntelligentManufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Shao SL,Wang T,Wang YL,et al. Research of HRV as a Measure of Mental Workload in Human and Dual-Arm Robot Interaction[J]. ELECTRONICS,2020,9(12):1-17.
APA Shao SL,Wang T,Wang YL,Su Y,Song CH,&Yao C.(2020).Research of HRV as a Measure of Mental Workload in Human and Dual-Arm Robot Interaction.ELECTRONICS,9(12),1-17.
MLA Shao SL,et al."Research of HRV as a Measure of Mental Workload in Human and Dual-Arm Robot Interaction".ELECTRONICS 9.12(2020):1-17.
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