Influence mechanism of running sportswear fatigue based on BP neural network
Abstract The fatigue of running sportswear is reflected in the fatigue caused by the tightness of the tights on the skin surface of the limbs, trunk, and other parts during long-term running sports. However, the current research on the fatigue of running sportswear is not deep enough. Therefore, the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-08-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-021-00778-8 |
| _version_ | 1818607488773128192 |
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| author | Xiaoli Liu Zhibin Li |
| author_facet | Xiaoli Liu Zhibin Li |
| author_sort | Xiaoli Liu |
| collection | DOAJ |
| description | Abstract The fatigue of running sportswear is reflected in the fatigue caused by the tightness of the tights on the skin surface of the limbs, trunk, and other parts during long-term running sports. However, the current research on the fatigue of running sportswear is not deep enough. Therefore, the purpose of this study is to study the mechanism of the fatigue of running sportswear based on BP neural network. This article first takes sportswear as the starting point and uses the surface myoelectricity index as a physiological quantity as a means to combine clothing with sports medicine and sports physiology, breaking the traditional shackles of subjective assessment of fatigue, and giving play to the advantages of interdisciplinary to expand the new direction of the apparel industry and, secondly, use muscle fatigue evaluation method to analyze the muscles of the lower leg under the pressure of sportswear to analyze the strength of the EMG signal and the number of participating sports units and the frequency of discharge synchronization. Experimental data shows that the AUC is 0.756 when wearing sports tights, the sensitivity and specificity are 72% and 19%, and the accuracy is 65%. The experimental results show that clothing pressure affects the fatigue of running sportswear based on BP neural network. |
| first_indexed | 2024-12-16T14:27:33Z |
| format | Article |
| id | doaj.art-5d55b73c850d43848accd94626fca7c3 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-12-16T14:27:33Z |
| publishDate | 2021-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-5d55b73c850d43848accd94626fca7c32022-12-21T22:28:20ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802021-08-012021111510.1186/s13634-021-00778-8Influence mechanism of running sportswear fatigue based on BP neural networkXiaoli Liu0Zhibin Li1Department of Physical Education, Shanghai University of Electric PowerCollege of Automation Engineering, Shanghai University of Electric PowerAbstract The fatigue of running sportswear is reflected in the fatigue caused by the tightness of the tights on the skin surface of the limbs, trunk, and other parts during long-term running sports. However, the current research on the fatigue of running sportswear is not deep enough. Therefore, the purpose of this study is to study the mechanism of the fatigue of running sportswear based on BP neural network. This article first takes sportswear as the starting point and uses the surface myoelectricity index as a physiological quantity as a means to combine clothing with sports medicine and sports physiology, breaking the traditional shackles of subjective assessment of fatigue, and giving play to the advantages of interdisciplinary to expand the new direction of the apparel industry and, secondly, use muscle fatigue evaluation method to analyze the muscles of the lower leg under the pressure of sportswear to analyze the strength of the EMG signal and the number of participating sports units and the frequency of discharge synchronization. Experimental data shows that the AUC is 0.756 when wearing sports tights, the sensitivity and specificity are 72% and 19%, and the accuracy is 65%. The experimental results show that clothing pressure affects the fatigue of running sportswear based on BP neural network.https://doi.org/10.1186/s13634-021-00778-8BP neural networkFatigue of running sportswearMuscle fatigue evaluationEigenvalue of fatigueBP neural network model |
| spellingShingle | Xiaoli Liu Zhibin Li Influence mechanism of running sportswear fatigue based on BP neural network EURASIP Journal on Advances in Signal Processing BP neural network Fatigue of running sportswear Muscle fatigue evaluation Eigenvalue of fatigue BP neural network model |
| title | Influence mechanism of running sportswear fatigue based on BP neural network |
| title_full | Influence mechanism of running sportswear fatigue based on BP neural network |
| title_fullStr | Influence mechanism of running sportswear fatigue based on BP neural network |
| title_full_unstemmed | Influence mechanism of running sportswear fatigue based on BP neural network |
| title_short | Influence mechanism of running sportswear fatigue based on BP neural network |
| title_sort | influence mechanism of running sportswear fatigue based on bp neural network |
| topic | BP neural network Fatigue of running sportswear Muscle fatigue evaluation Eigenvalue of fatigue BP neural network model |
| url | https://doi.org/10.1186/s13634-021-00778-8 |
| work_keys_str_mv | AT xiaoliliu influencemechanismofrunningsportswearfatiguebasedonbpneuralnetwork AT zhibinli influencemechanismofrunningsportswearfatiguebasedonbpneuralnetwork |