CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Prediction of Human Voluntary Torques Based on Collaborative Neuromusculoskeletal Modeling and Adaptive Learning
Wang, Weiqun1,2; Shi, Weiguo1,2; Hou, Zeng-Guang1,2,3; Chen, Badong4; Liang, Xu1,2; Ren, Shixin1,2; Wang, Jiaxing1,2; Peng, Liang1,2
Source PublicationIEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
ISSN0278-0046
2021-06-01
Volume68Issue:6Pages:5217-5226
Abstract

Surface Electromyography (sEMG) based human-robot interaction has been widely studied, where prediction of human voluntary torques is one of the key issues that have not been well addressed. In this article, a torque prediction method based on collaborative neuromusculoskeletal modeling and adaptive learning, is proposed to overcome the limitation of existing methods. First, an sEMG-torque model is designed in comprehensive consideration of the previous research results, the requirement for subject-specific adjustment and the coupling between the muscle or muscle-tendon length and the adjacent joint angles, where the latter two factors have rarely been considered in the literature. Then, by combining the advantages of the stochastic particle swarm optimization and conjugate gradient algorithms, a collaborative optimization method is designed to calibrate simultaneously the undetermined parameters. Moreover, an adaptive learning method based on Gaussian process regression is proposed to learn and predict the estimation errors in real time, by which it is supposed that the torque prediction accuracy can be improved efficiently. Finally, experiments were carried out to validate the performance of the proposed method.

KeywordMuscles Adaptation models Adaptive learning Force Calibration Hip Electromyography Adaptive learning human– robot interaction neuromusculoskeletal modeling parameter calibration surface electromyography (sEMG) processing
DOI10.1109/TIE.2020.2991999
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[91648208] ; National Natural Science Foundation of China[91848110] ; National Natural Science Foundation of China[U1913601] ; National Key R&D Program of China[2017YFB1302303] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32000000]
Funding OrganizationNational Natural Science Foundation of China ; National Key R&D Program of China ; Strategic Priority Research Program of Chinese Academy of Science
WOS Research AreaAutomation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000621470900060
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/43318
Collection复杂系统管理与控制国家重点实验室_先进机器人
Corresponding AuthorHou, Zeng-Guang
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
4.Jiaotong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Weiqun,Shi, Weiguo,Hou, Zeng-Guang,et al. Prediction of Human Voluntary Torques Based on Collaborative Neuromusculoskeletal Modeling and Adaptive Learning[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2021,68(6):5217-5226.
APA Wang, Weiqun.,Shi, Weiguo.,Hou, Zeng-Guang.,Chen, Badong.,Liang, Xu.,...&Peng, Liang.(2021).Prediction of Human Voluntary Torques Based on Collaborative Neuromusculoskeletal Modeling and Adaptive Learning.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,68(6),5217-5226.
MLA Wang, Weiqun,et al."Prediction of Human Voluntary Torques Based on Collaborative Neuromusculoskeletal Modeling and Adaptive Learning".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 68.6(2021):5217-5226.
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