Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network

doi:10.1109/JBHI.2022.3198640

HFCAS OpenIR

	Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network
	Wang, Shurun 1; Tang, Hao 1; Gao, Lifu2 ; Tan, Qi 1
	2022-11-01
发表期刊	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
ISSN	2168-2194
通讯作者	Tang, Hao(htang@hfut.edu.cn)
摘要	Intention recognition based on surface electromyography (sEMG) signals is pivotal in human-machine interaction (HMI), where continuous motion estimation with high accuracy has been the challenge. The convolutional neural network (CNN) possesses excellent feature extraction capability. Still, it is difficult for ordinary CNN to explore the dependencies of time-series data, so most researchers adopt the recurrent neural network or its variants (e.g., LSTM) for motion estimation tasks. This paper proposes a multi-feature temporal convolutional attention-based network (MFTCAN) to recognize joint angles continuously. First, we recruited ten subjects to accomplish the signal acquisition experiments in different motion patterns. Then, we developed a joint training mechanism that integrates MFTCAN with commonly used statistical algorithms, and the integrated architectures were named MFTCAN-KNR, MFTCAN-SVR and MFTCAN-LR. Last, we utilized two performance indicators (RMSE and R-2) to evaluate the effect of different methods. Moreover, we further validated the performance of the proposed method on the open dataset (Ninapro DB2). When evaluating on the original dataset, the average RMSE of the estimations obtained by MFTCAN-KNR is 0.14, which is significantly less than the results obtained by LSTM (0.20) and BP (0.21). The average R-2 of the estimations obtained by MFTCAN-KNR is 0.87, indicating the anti-disturbance ability of the architecture. Moreover, MFTCAN-KNR also achieves high performance when evaluating on the open dataset. The proposed methods can effectively accomplish the task of motion estimation, allowing further implementations in the human-exoskeleton interaction systems.
关键词	Attention mechanism motion estimation surface electromyography temporal convolutional network
DOI	10.1109/JBHI.2022.3198640
关键词[WOS]	MOVEMENTS
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2017YFE0129700]
项目资助者	National Key R&D Program of China
WOS研究方向	Computer Science ; Mathematical & Computational Biology ; Medical Informatics
WOS类目	Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS记录号	WOS:000882005700023
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/130344
专题	中国科学院合肥物质科学研究院
通讯作者	Tang, Hao
作者单位	1.Hefei Univ Technol, Sch Elect Engn & Automat, Hefei 230009, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China
推荐引用方式 GB/T 7714	Wang, Shurun,Tang, Hao,Gao, Lifu,et al. Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2022,26.
APA	Wang, Shurun,Tang, Hao,Gao, Lifu,&Tan, Qi.(2022).Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,26.
MLA	Wang, Shurun,et al."Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 26(2022).