Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle
Abstract The gait analysis has been applied in many fields, such as the assessment of falling, force evaluation in sports, and gait disorder detection for neuromuscular diseases. Its main recording techniques include video cameras and wearable sensors. However, the present methods involve measuring...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-95973-0 |
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| author | Shing-Hong Liu Alok Kumar Sharma Bo-Yan Wu Xin Zhu Chun-Ju Chang Jia-Jung Wang |
| author_facet | Shing-Hong Liu Alok Kumar Sharma Bo-Yan Wu Xin Zhu Chun-Ju Chang Jia-Jung Wang |
| author_sort | Shing-Hong Liu |
| collection | DOAJ |
| description | Abstract The gait analysis has been applied in many fields, such as the assessment of falling, force evaluation in sports, and gait disorder detection for neuromuscular diseases. Its main recording techniques include video cameras and wearable sensors. However, the present methods involve measuring surface electromyograms (sEMGs) to analyze muscle activities. The primary goal of this study is to estimate gait parameters under different power capacity of muscle by sEMGs measured from lower limbs. A self-made wireless device recorded sEMGs from two muscles of each foot, and GaitUp Physilog®5 sensors captured gait parameters from 18 participants under running as references. Four features including median frequency (MDF), waveform length (WL), standard deviation (SD), and sample entropy (SampEn), were extracted from the sEMG data. The analysis utilized three machine learning models (Random Forest, CatBoost, XGBoost), evaluated through various evaluation metrics. Additionally, 5-fold cross-validation was conducted to assess the influence of muscle fatigue on the estimation of gait parameters. The results show that all models successfully estimated 20 gait parameters, all showing a Pearson correlation coefficient (PCC) above 0.800. However, the performance of models significantly depends on the condition of muscle fatigue. This study represents a significant advancement in gait analysis, providing a comprehensive method for estimating gait parameters from sEMG signals, with important implications for mobile health applications. |
| format | Article |
| id | doaj-art-c90eeda9e59848ca9a3d3db7e70d1295 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-c90eeda9e59848ca9a3d3db7e70d12952025-08-20T03:10:29ZengNature PortfolioScientific Reports2045-23222025-04-0115111510.1038/s41598-025-95973-0Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscleShing-Hong Liu0Alok Kumar Sharma1Bo-Yan Wu2Xin Zhu3Chun-Ju Chang4Jia-Jung Wang5Department of Computer Science and Information Engineering, Chaoyang University of TechnologyDepartment of Computer Science and Information Engineering, Chaoyang University of TechnologyDepartment of Computer Science and Information Engineering, Chaoyang University of TechnologyDepartment of AI Technology Development, M&D Data Science Center, Institute of Integrated Research, Institute of Science TokyoDepartment of Golden-Ager Industry Management, Chaoyang University of TechnologyDepartment of Biomedical Engineering, I-Shou UniversityAbstract The gait analysis has been applied in many fields, such as the assessment of falling, force evaluation in sports, and gait disorder detection for neuromuscular diseases. Its main recording techniques include video cameras and wearable sensors. However, the present methods involve measuring surface electromyograms (sEMGs) to analyze muscle activities. The primary goal of this study is to estimate gait parameters under different power capacity of muscle by sEMGs measured from lower limbs. A self-made wireless device recorded sEMGs from two muscles of each foot, and GaitUp Physilog®5 sensors captured gait parameters from 18 participants under running as references. Four features including median frequency (MDF), waveform length (WL), standard deviation (SD), and sample entropy (SampEn), were extracted from the sEMG data. The analysis utilized three machine learning models (Random Forest, CatBoost, XGBoost), evaluated through various evaluation metrics. Additionally, 5-fold cross-validation was conducted to assess the influence of muscle fatigue on the estimation of gait parameters. The results show that all models successfully estimated 20 gait parameters, all showing a Pearson correlation coefficient (PCC) above 0.800. However, the performance of models significantly depends on the condition of muscle fatigue. This study represents a significant advancement in gait analysis, providing a comprehensive method for estimating gait parameters from sEMG signals, with important implications for mobile health applications.https://doi.org/10.1038/s41598-025-95973-0Surface electromyogramMachine learningRandom forestXGBoostCatBoostGait parameters |
| spellingShingle | Shing-Hong Liu Alok Kumar Sharma Bo-Yan Wu Xin Zhu Chun-Ju Chang Jia-Jung Wang Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle Scientific Reports Surface electromyogram Machine learning Random forest XGBoost CatBoost Gait parameters |
| title | Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle |
| title_full | Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle |
| title_fullStr | Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle |
| title_full_unstemmed | Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle |
| title_short | Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle |
| title_sort | estimating gait parameters from semg signals using machine learning techniques under different power capacity of muscle |
| topic | Surface electromyogram Machine learning Random forest XGBoost CatBoost Gait parameters |
| url | https://doi.org/10.1038/s41598-025-95973-0 |
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