The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training Strategy
Surface Electromyography (sEMG) enables an intuitive control of wearable robots. The muscle fatigue-induced changes of sEMG signals might limit the long-term usage of the sEMG-based control algorithms. This paper presents the performance deterioration of sEMG-based gait phase classifiers, explains t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/9/3821 |
| _version_ | 1797536615484096512 |
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| author | Jianfei Zhu Chunzhi Yi Baichun Wei Chifu Yang Zhen Ding Feng Jiang |
| author_facet | Jianfei Zhu Chunzhi Yi Baichun Wei Chifu Yang Zhen Ding Feng Jiang |
| author_sort | Jianfei Zhu |
| collection | DOAJ |
| description | Surface Electromyography (sEMG) enables an intuitive control of wearable robots. The muscle fatigue-induced changes of sEMG signals might limit the long-term usage of the sEMG-based control algorithms. This paper presents the performance deterioration of sEMG-based gait phase classifiers, explains the deterioration by analyzing the time-varying changes of the extracted features, and proposes a training strategy that can improve the classifiers’ robustness against muscle fatigue. In particular, we first select some features that are commonly used in fatigue-related studies and use them to classify gait phases under muscle fatigue. Then, we analyze the time-varying characteristics of extracted features, with the aim of explaining the performance of the classifiers. Finally, we propose a training strategy that effectively improves the robustness against muscle fatigue, which contributes to an easy-to-use method. Ten subjects performing prolonged walking are recruited. Our study contributes to a novel perspective of designing gait phase classifiers under muscle fatigue. |
| first_indexed | 2024-03-10T12:03:15Z |
| format | Article |
| id | doaj.art-d95904f37da84474a3535d4d6843b5cf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T12:03:15Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d95904f37da84474a3535d4d6843b5cf2023-11-21T16:50:59ZengMDPI AGApplied Sciences2076-34172021-04-01119382110.3390/app11093821The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training StrategyJianfei Zhu0Chunzhi Yi1Baichun Wei2Chifu Yang3Zhen Ding4Feng Jiang5School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, ChinaSurface Electromyography (sEMG) enables an intuitive control of wearable robots. The muscle fatigue-induced changes of sEMG signals might limit the long-term usage of the sEMG-based control algorithms. This paper presents the performance deterioration of sEMG-based gait phase classifiers, explains the deterioration by analyzing the time-varying changes of the extracted features, and proposes a training strategy that can improve the classifiers’ robustness against muscle fatigue. In particular, we first select some features that are commonly used in fatigue-related studies and use them to classify gait phases under muscle fatigue. Then, we analyze the time-varying characteristics of extracted features, with the aim of explaining the performance of the classifiers. Finally, we propose a training strategy that effectively improves the robustness against muscle fatigue, which contributes to an easy-to-use method. Ten subjects performing prolonged walking are recruited. Our study contributes to a novel perspective of designing gait phase classifiers under muscle fatigue.https://www.mdpi.com/2076-3417/11/9/3821electromyographygait phase classificationmuscle fatiguewearable robots |
| spellingShingle | Jianfei Zhu Chunzhi Yi Baichun Wei Chifu Yang Zhen Ding Feng Jiang The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training Strategy Applied Sciences electromyography gait phase classification muscle fatigue wearable robots |
| title | The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training Strategy |
| title_full | The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training Strategy |
| title_fullStr | The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training Strategy |
| title_full_unstemmed | The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training Strategy |
| title_short | The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training Strategy |
| title_sort | muscle fatigue s effects on the semg based gait phase classification an experimental study and a novel training strategy |
| topic | electromyography gait phase classification muscle fatigue wearable robots |
| url | https://www.mdpi.com/2076-3417/11/9/3821 |
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