Research on the Recognition of Various Muscle Fatigue States in Resistance Strength Training
Instantly and accurately identifying the state of dynamic muscle fatigue in resistance training can help fitness trainers to build a more scientific and reasonable training program. By investigating the isokinetic flexion and extension strength training of the knee joint, this paper tried to extract...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/11/2292 |
| _version_ | 1797465242705330176 |
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| author | Yinghao Wang Chunfu Lu Mingyu Zhang Jianfeng Wu Zhichuan Tang |
| author_facet | Yinghao Wang Chunfu Lu Mingyu Zhang Jianfeng Wu Zhichuan Tang |
| author_sort | Yinghao Wang |
| collection | DOAJ |
| description | Instantly and accurately identifying the state of dynamic muscle fatigue in resistance training can help fitness trainers to build a more scientific and reasonable training program. By investigating the isokinetic flexion and extension strength training of the knee joint, this paper tried to extract surface electromyogram (sEMG) features and establish recognition models to classify muscle states of the target muscles in the isokinetic strength training of the knee joint. First, an experiment was carried out to collect the sEMG signals of the target muscles. Second, two nonlinear dynamic indexes, wavelet packet entropy (WPE) and power spectrum entropy (PSE), were extracted from the obtained sEMG signals to verify the feasibility of characterizing muscle fatigue. Third, a convolutional neural network (CNN) recognition model was constructed and trained with the obtained sEMG experimental data to enable the extraction and recognition of EMG deep features. Finally, the CNN recognition model was compared with multiple support vector machines (Multi-SVM) and multiple linear discriminant analysis (Multi-LDA). The results showed that the CNN model had a better classification accuracy. The overall recognition accuracy of the CNN model applied to the test data (91.38%) was higher than that of the other two models, which verified that the CNN dynamic fatigue recognition model based on subjective and objective information feedback had better recognition performance. Furthermore, training on a larger dataset could further improve the recognition accuracy of the CNN recognition model. |
| first_indexed | 2024-03-09T18:18:44Z |
| format | Article |
| id | doaj.art-83ccc3dd0dba47cb9418eeb79cbc0b9e |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T18:18:44Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-83ccc3dd0dba47cb9418eeb79cbc0b9e2023-11-24T08:28:16ZengMDPI AGHealthcare2227-90322022-11-011011229210.3390/healthcare10112292Research on the Recognition of Various Muscle Fatigue States in Resistance Strength TrainingYinghao Wang0Chunfu Lu1Mingyu Zhang2Jianfeng Wu3Zhichuan Tang4Industrial Design Department, Zhejiang University of Technology, Hangzhou 310023, ChinaIndustrial Design Department, Zhejiang University of Technology, Hangzhou 310023, ChinaIndustrial Design Department, Zhejiang University of Technology, Hangzhou 310023, ChinaIndustrial Design Department, Zhejiang University of Technology, Hangzhou 310023, ChinaIndustrial Design Department, Zhejiang University of Technology, Hangzhou 310023, ChinaInstantly and accurately identifying the state of dynamic muscle fatigue in resistance training can help fitness trainers to build a more scientific and reasonable training program. By investigating the isokinetic flexion and extension strength training of the knee joint, this paper tried to extract surface electromyogram (sEMG) features and establish recognition models to classify muscle states of the target muscles in the isokinetic strength training of the knee joint. First, an experiment was carried out to collect the sEMG signals of the target muscles. Second, two nonlinear dynamic indexes, wavelet packet entropy (WPE) and power spectrum entropy (PSE), were extracted from the obtained sEMG signals to verify the feasibility of characterizing muscle fatigue. Third, a convolutional neural network (CNN) recognition model was constructed and trained with the obtained sEMG experimental data to enable the extraction and recognition of EMG deep features. Finally, the CNN recognition model was compared with multiple support vector machines (Multi-SVM) and multiple linear discriminant analysis (Multi-LDA). The results showed that the CNN model had a better classification accuracy. The overall recognition accuracy of the CNN model applied to the test data (91.38%) was higher than that of the other two models, which verified that the CNN dynamic fatigue recognition model based on subjective and objective information feedback had better recognition performance. Furthermore, training on a larger dataset could further improve the recognition accuracy of the CNN recognition model.https://www.mdpi.com/2227-9032/10/11/2292resistance strength trainingdynamic muscle fatiguerecognitionsEMG signalconvolutional neural network |
| spellingShingle | Yinghao Wang Chunfu Lu Mingyu Zhang Jianfeng Wu Zhichuan Tang Research on the Recognition of Various Muscle Fatigue States in Resistance Strength Training Healthcare resistance strength training dynamic muscle fatigue recognition sEMG signal convolutional neural network |
| title | Research on the Recognition of Various Muscle Fatigue States in Resistance Strength Training |
| title_full | Research on the Recognition of Various Muscle Fatigue States in Resistance Strength Training |
| title_fullStr | Research on the Recognition of Various Muscle Fatigue States in Resistance Strength Training |
| title_full_unstemmed | Research on the Recognition of Various Muscle Fatigue States in Resistance Strength Training |
| title_short | Research on the Recognition of Various Muscle Fatigue States in Resistance Strength Training |
| title_sort | research on the recognition of various muscle fatigue states in resistance strength training |
| topic | resistance strength training dynamic muscle fatigue recognition sEMG signal convolutional neural network |
| url | https://www.mdpi.com/2227-9032/10/11/2292 |
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