Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force Estimation
Surface electromyography- (sEMG-) based hand grasp force estimation plays an important role with a promising accuracy in a laboratory environment, yet hardly clinically applicable because of physiological changes and other factors. One of the critical factors is the muscle fatigue concomitant with d...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2021/8817480 |
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| _version_ | 1832565972636008448 |
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| author | Jinfeng Wang Muye Pang Peixuan Yu Biwei Tang Kui Xiang Zhaojie Ju |
| author_facet | Jinfeng Wang Muye Pang Peixuan Yu Biwei Tang Kui Xiang Zhaojie Ju |
| author_sort | Jinfeng Wang |
| collection | DOAJ |
| description | Surface electromyography- (sEMG-) based hand grasp force estimation plays an important role with a promising accuracy in a laboratory environment, yet hardly clinically applicable because of physiological changes and other factors. One of the critical factors is the muscle fatigue concomitant with daily activities which degrades the accuracy and reliability of force estimation from sEMG signals. Conventional qualitative measurements of muscle fatigue contribute to an improved force estimation model with limited progress. This paper proposes an easy-to-implement method to evaluate the muscle fatigue quantitatively and demonstrates that the proposed metrics can have a substantial impact on improving the performance of hand grasp force estimation. Specifically, the reduction in the maximal capacity to generate force is used as the metric of muscle fatigue in combination with a back-propagation neural network (BPNN) is adopted to build a sEMG-hand grasp force estimation model. Experiments are conducted in the three cases: (1) pooling training data from all muscle fatigue states with time-domain feature only, (2) employing frequency domain feature for expression of muscle fatigue information based on case 1, and 3) incorporating the quantitative metric of muscle fatigue value as an additional input for estimation model based on case 1. The results show that the degree of muscle fatigue and task intensity can be easily distinguished, and the additional input of muscle fatigue in BPNN greatly improves the performance of hand grasp force estimation, which is reflected by the 6.3797% increase in R2 (coefficient of determination) value. |
| format | Article |
| id | doaj-art-12a7be3a12f3496ea0ebf6494f32b748 |
| institution | Kabale University |
| issn | 1176-2322 1754-2103 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-12a7be3a12f3496ea0ebf6494f32b7482025-02-03T01:05:25ZengWileyApplied Bionics and Biomechanics1176-23221754-21032021-01-01202110.1155/2021/88174808817480Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force EstimationJinfeng Wang0Muye Pang1Peixuan Yu2Biwei Tang3Kui Xiang4Zhaojie Ju5Department of Information, Wuhan Huaxia University of Technology, 430223 Wuhan, ChinaIntelligent System Research Institute, Wuhan University of Technology, 430070 Wuhan, ChinaIntelligent System Research Institute, Wuhan University of Technology, 430070 Wuhan, ChinaIntelligent System Research Institute, Wuhan University of Technology, 430070 Wuhan, ChinaIntelligent System Research Institute, Wuhan University of Technology, 430070 Wuhan, ChinaIntelligent System & Biomedical Robotics Group, University of Portsmouth, PO1 3HE Portsmouth, UKSurface electromyography- (sEMG-) based hand grasp force estimation plays an important role with a promising accuracy in a laboratory environment, yet hardly clinically applicable because of physiological changes and other factors. One of the critical factors is the muscle fatigue concomitant with daily activities which degrades the accuracy and reliability of force estimation from sEMG signals. Conventional qualitative measurements of muscle fatigue contribute to an improved force estimation model with limited progress. This paper proposes an easy-to-implement method to evaluate the muscle fatigue quantitatively and demonstrates that the proposed metrics can have a substantial impact on improving the performance of hand grasp force estimation. Specifically, the reduction in the maximal capacity to generate force is used as the metric of muscle fatigue in combination with a back-propagation neural network (BPNN) is adopted to build a sEMG-hand grasp force estimation model. Experiments are conducted in the three cases: (1) pooling training data from all muscle fatigue states with time-domain feature only, (2) employing frequency domain feature for expression of muscle fatigue information based on case 1, and 3) incorporating the quantitative metric of muscle fatigue value as an additional input for estimation model based on case 1. The results show that the degree of muscle fatigue and task intensity can be easily distinguished, and the additional input of muscle fatigue in BPNN greatly improves the performance of hand grasp force estimation, which is reflected by the 6.3797% increase in R2 (coefficient of determination) value.http://dx.doi.org/10.1155/2021/8817480 |
| spellingShingle | Jinfeng Wang Muye Pang Peixuan Yu Biwei Tang Kui Xiang Zhaojie Ju Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force Estimation Applied Bionics and Biomechanics |
| title | Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force Estimation |
| title_full | Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force Estimation |
| title_fullStr | Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force Estimation |
| title_full_unstemmed | Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force Estimation |
| title_short | Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force Estimation |
| title_sort | effect of muscle fatigue on surface electromyography based hand grasp force estimation |
| url | http://dx.doi.org/10.1155/2021/8817480 |
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