Recognition of Muscle Fatigue Status Based on Improved Wavelet Threshold and CNN-SVM

doi:10.1109/ACCESS.2020.3038422

	Recognition of Muscle Fatigue Status Based on Improved Wavelet Threshold and CNN-SVM
	Wang, Junhong 1,2; Sun, Yining 1,2; Sun, Shaoming 1,3
刊名	IEEE ACCESS
	2020
卷号	8
关键词	Muscles Fatigue Feature extraction Wavelet analysis Support vector machines Noise reduction Training Convolutional neural network-support vector machine (CNN-SVM) muscle fatigue sEMG wavelet threshold
ISSN号	2169-3536
DOI	10.1109/ACCESS.2020.3038422
通讯作者	Sun, Yining(ynsun@iim.cas.cn)
英文摘要	This study proposed a muscle fatigue classification method based on surface electromyography (sEMG) signals to achieve accurate muscle fatigue detection and classification. A total of 20 healthy young participants (14 men and 6 women) were recruited for fatigue testing on a cycle ergometer, and sEMG signals and oxygen uptake were recorded during the test. First, the measured sEMG signals were denoised with an improved wavelet threshold method. Second, the V-slope method was used to identify the ventilation threshold (VT) to reflect the muscle fatigue state. The time- and frequency-domain features of the sEMG signals were extracted, including root mean square, integrated electromyography, median frequency, mean power frequency, and band spectral entropy. Third, the time- and frequency-domain features of the sEMG signals were labeled either "normal" or "fatigued" based on the VT. Finally, the statistical features of 16 participants were selected as the training data set of the Convolutional Neural Network-Support Vector Machine (CNN-SVM), Support Vector Machine, Convolutional Neural Network, and Particle Swarm Optimization-Support Vector Machine algorithms. In addition, the statistical features of the four remaining participants were used as the test data set to analyze the classification accuracy of the four aforementioned algorithms. Experimental results indicated that the denoising effect of the improved wavelet threshold algorithm proposed in this study was satisfactory. The CNN-SVM algorithm achieved accurate muscle fatigue classification and 80.33%-86.69% classification accuracy.
资助项目	National Key Research and Development Program of China[2018YFC2001304] ; Science and Technology Major Project of Anhui[17030901021]
WOS关键词	SIGNAL ; CLASSIFICATION ; TRANSFORM
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000594448900001
资助机构	National Key Research and Development Program of China ; Science and Technology Major Project of Anhui
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/105400]
专题	中国科学院合肥物质科学研究院
通讯作者	Sun, Yining
作者单位	1.Univ Sci & Technol China, Hefei Inst Phys Sci, Hefei 230026, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 3.Chinese Acad Sci, Inst Technol Innovat, Hefei 230088, Peoples R China
推荐引用方式 GB/T 7714	Wang, Junhong,Sun, Yining,Sun, Shaoming. Recognition of Muscle Fatigue Status Based on Improved Wavelet Threshold and CNN-SVM[J]. IEEE ACCESS,2020,8.
APA	Wang, Junhong,Sun, Yining,&Sun, Shaoming.(2020).Recognition of Muscle Fatigue Status Based on Improved Wavelet Threshold and CNN-SVM.IEEE ACCESS,8.
MLA	Wang, Junhong,et al."Recognition of Muscle Fatigue Status Based on Improved Wavelet Threshold and CNN-SVM".IEEE ACCESS 8(2020).