Recognition of Muscle Fatigue Status Based on Improved Wavelet Threshold and CNN-SVM
文献类型：期刊论文
| 作者 | Wang, Junhong2,3; Sun, Yining2,3 ; Sun, Shaoming1,2
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| 刊名 | IEEE ACCESS
 |
| 出版日期 | 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. |
| WOS关键词 | SIGNAL ; CLASSIFICATION ; TRANSFORM |
| 资助项目 | National Key Research and Development Program of China[2018YFC2001304] ; Science and Technology Major Project of Anhui[17030901021] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000594448900001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; Science and Technology Major Project of Anhui |
| 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.Chinese Acad Sci, Inst Technol Innovat, Hefei 230088, Peoples R China 2.Univ Sci & Technol China, Hefei Inst Phys Sci, Hefei 230026, Peoples R China 3.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, 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). |
入库方式： OAI收割
来源：合肥物质科学研究院
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