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A Muscle Fatigue Classification Model Based on LSTM and Improved Wavelet Packet Threshold

文献类型：期刊论文

作者Wang, Junhong1,2; Sun, Shaoming1,2; Sun, Yining1,2
刊名SENSORS
出版日期2021-10-01
卷号21
关键词surface electromyography wavelet packet muscle fatigue long short-term memory
DOI10.3390/s21196369
通讯作者Sun, Shaoming(smsun@iim.ac.cn)
英文摘要Previous studies have used the anaerobic threshold (AT) to non-invasively predict muscle fatigue. This study proposes a novel method for the automatic classification of muscle fatigue based on surface electromyography (sEMG). The sEMG data were acquired from 20 participants during an incremental test on a cycle ergometer using sEMG sensors placed on the vastus rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), and gastrocnemius (GA) muscles of the left leg. The ventilation volume (VE), oxygen uptake (VO2), and carbon dioxide production (VCO2) data of each participant were collected during the test. Then, we extracted the time-domain and frequency-domain features of the sEMG signal denoised by the improved wavelet packet threshold denoising algorithm. In this study, we propose a new muscle fatigue recognition model based on the long short-term memory (LSTM) network. The LSTM network was trained to classify muscle fatigue using sEMG signal features. The results showed that the improved wavelet packet threshold function has better performance in denoising sEMG signals than hard threshold and soft threshold functions. The classification performance of the muscle fatigue recognition model proposed in this paper is better than that of CNN (convolutional neural network), SVM (support vector machine), and the classification models proposed by other scholars. The best performance of the LSTM network was achieved with 70% training, 10% validation, and 20% testing rates. Generally, the proposed model can be used to monitor muscle fatigue.

WOS关键词SURFACE EMG ; SIGNAL ; CONTRACTION ; TRANSFORM
资助项目Anhui Provincial Key Research and Development Plan[202004a07020037]
WOS研究方向Chemistry ; Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:000707861600001
出版者MDPI
资助机构Anhui Provincial Key Research and Development Plan
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/125631]  
专题中国科学院合肥物质科学研究院
通讯作者Sun, Shaoming
作者单位1.Univ Sci & Technol China, Hefei 230026, Peoples R China
2.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Junhong,Sun, Shaoming,Sun, Yining. A Muscle Fatigue Classification Model Based on LSTM and Improved Wavelet Packet Threshold[J]. SENSORS,2021,21.
APA Wang, Junhong,Sun, Shaoming,&Sun, Yining.(2021).A Muscle Fatigue Classification Model Based on LSTM and Improved Wavelet Packet Threshold.SENSORS,21.
MLA Wang, Junhong,et al."A Muscle Fatigue Classification Model Based on LSTM and Improved Wavelet Packet Threshold".SENSORS 21(2021).

入库方式： OAI收割

来源：合肥物质科学研究院

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