Surface Electromyography Monitoring of Muscle Changes in Male Basketball Players During Isotonic Training
Physiological indicators are increasingly employed in sports training. However, studies on surface electromyography (sEMG) primarily focus on the analysis of isometric contraction. Research on sEMG related to isotonic contraction, which is more relevant to athletic performance, remains relatively li...
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MDPI AG
2025-02-01
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| author | Ziyang Li Bowen Zhang Hong Wang Mohamed Amin Gouda |
| author_facet | Ziyang Li Bowen Zhang Hong Wang Mohamed Amin Gouda |
| author_sort | Ziyang Li |
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| description | Physiological indicators are increasingly employed in sports training. However, studies on surface electromyography (sEMG) primarily focus on the analysis of isometric contraction. Research on sEMG related to isotonic contraction, which is more relevant to athletic performance, remains relatively limited. This paper examines the changes in the isotonic contraction performance of the male upper arm muscles resulting from long-term basketball training using the sEMG metrics. We recruited basketball physical education (B-PE) and non-PE majors to conduct a controlled isotonic contraction experiment to collect and analyze sEMG signals. The sample entropy event detection method was utilized to extract the epochs of active segments of data. Subsequently, statistical analysis methods were applied to extract the key sEMG time domain (TD) and frequency domain (FD) features of isotonic contraction that can differentiate between professional and amateur athletes. Machine learning methods were employed to perform ten-fold cross-validation and repeated experiments to verify the effectiveness of the features across the different groups. This paper investigates the key features and channels of interest for categorizing male participants from non-PE and B-PE backgrounds. The experimental results show that the F12B feature group consistently achieved an accuracy of between 80% and 90% with the SVM2 model, balancing both accuracy and efficiency, which can serve as evaluation indices for isotonic contraction performance of upper limb muscles during basketball training. This has practical significance for monitoring isotonic sEMG features in sports and training, as well as for providing individualized training regimens. |
| format | Article |
| id | doaj-art-2f816aca4f354b4699a0e598faee8431 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-2f816aca4f354b4699a0e598faee84312025-08-20T02:53:23ZengMDPI AGSensors1424-82202025-02-01255135510.3390/s25051355Surface Electromyography Monitoring of Muscle Changes in Male Basketball Players During Isotonic TrainingZiyang Li0Bowen Zhang1Hong Wang2Mohamed Amin Gouda3Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, ChinaPhysical Education Department, Northeastern University, Wenhua Street, Shenyang 110819, ChinaDepartment of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, ChinaDepartment of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, ChinaPhysiological indicators are increasingly employed in sports training. However, studies on surface electromyography (sEMG) primarily focus on the analysis of isometric contraction. Research on sEMG related to isotonic contraction, which is more relevant to athletic performance, remains relatively limited. This paper examines the changes in the isotonic contraction performance of the male upper arm muscles resulting from long-term basketball training using the sEMG metrics. We recruited basketball physical education (B-PE) and non-PE majors to conduct a controlled isotonic contraction experiment to collect and analyze sEMG signals. The sample entropy event detection method was utilized to extract the epochs of active segments of data. Subsequently, statistical analysis methods were applied to extract the key sEMG time domain (TD) and frequency domain (FD) features of isotonic contraction that can differentiate between professional and amateur athletes. Machine learning methods were employed to perform ten-fold cross-validation and repeated experiments to verify the effectiveness of the features across the different groups. This paper investigates the key features and channels of interest for categorizing male participants from non-PE and B-PE backgrounds. The experimental results show that the F12B feature group consistently achieved an accuracy of between 80% and 90% with the SVM2 model, balancing both accuracy and efficiency, which can serve as evaluation indices for isotonic contraction performance of upper limb muscles during basketball training. This has practical significance for monitoring isotonic sEMG features in sports and training, as well as for providing individualized training regimens.https://www.mdpi.com/1424-8220/25/5/1355electromyographyfeature extractionresistance trainingmuscle changesmachine learning |
| spellingShingle | Ziyang Li Bowen Zhang Hong Wang Mohamed Amin Gouda Surface Electromyography Monitoring of Muscle Changes in Male Basketball Players During Isotonic Training Sensors electromyography feature extraction resistance training muscle changes machine learning |
| title | Surface Electromyography Monitoring of Muscle Changes in Male Basketball Players During Isotonic Training |
| title_full | Surface Electromyography Monitoring of Muscle Changes in Male Basketball Players During Isotonic Training |
| title_fullStr | Surface Electromyography Monitoring of Muscle Changes in Male Basketball Players During Isotonic Training |
| title_full_unstemmed | Surface Electromyography Monitoring of Muscle Changes in Male Basketball Players During Isotonic Training |
| title_short | Surface Electromyography Monitoring of Muscle Changes in Male Basketball Players During Isotonic Training |
| title_sort | surface electromyography monitoring of muscle changes in male basketball players during isotonic training |
| topic | electromyography feature extraction resistance training muscle changes machine learning |
| url | https://www.mdpi.com/1424-8220/25/5/1355 |
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