{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T02:38:45Z","timestamp":1790476725197,"version":"4.1.0"},"reference-count":48,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:00:00Z","timestamp":1740182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R &amp; D Program of China","award":["2021YFF0306405"],"award-info":[{"award-number":["2021YFF0306405"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s25051355","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T05:36:47Z","timestamp":1740375407000},"page":"1355","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Surface Electromyography Monitoring of Muscle Changes in Male Basketball Players During Isotonic Training"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0371-6341","authenticated-orcid":false,"given":"Ziyang","family":"Li","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Physical Education Department, Northeastern University, Wenhua Street, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7639-6967","authenticated-orcid":false,"given":"Hong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6198-3563","authenticated-orcid":false,"given":"Mohamed Amin","family":"Gouda","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2641","DOI":"10.1519\/JSC.0b013e31823f8dd4","article-title":"The effects of resistance training on explosive strength indicators in adolescent basketball players","volume":"26","author":"Santos","year":"2012","journal-title":"J. Strength Cond. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1519\/SSC.0b013e31817711a4","article-title":"A Brief Review: How Much Rest between Sets?","volume":"30","author":"Willardson","year":"2008","journal-title":"Strength Cond. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.smhs.2022.09.001","article-title":"The influence of resistance training on adipokines in post-menopausal women: A brief review","volume":"4","author":"Wilburn","year":"2022","journal-title":"Sports Med. Health Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bansal, D. (2021). Real-Time Data Acquisition in Human Physiology, Elsevier.","DOI":"10.1016\/B978-0-12-822118-1.00002-3"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Nazmi, N., Abdul Rahman, M., Yamamoto, S.I., Ahmad, S., Zamzuri, H., and Mazlan, S. (2016). A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions. Sensors, 16.","DOI":"10.3390\/s16081304"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"161","DOI":"10.59175\/pijed.v3i2.280","article-title":"The Effect of Arm Muscle Strength and Leg Muscle Explosiveness on Ball Shooting Results Basketball","volume":"3","author":"Jumanza","year":"2024","journal-title":"PPSDP Int. J. Educ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1080\/02640414.2016.1210197","article-title":"Dose-response relationship between weekly resistance training volume and increases in muscle mass: A systematic review and meta-analysis","volume":"35","author":"Schoenfeld","year":"2016","journal-title":"J. Sports Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1519\/JSC.0000000000004329","article-title":"Narrative Review of Sex Differences in Muscle Strength, Endurance, Activation, Size, Fiber Type, and Strength Training Participation Rates, Preferences, Motivations, Injuries, and Neuromuscular Adaptations","volume":"37","author":"Nuzzo","year":"2022","journal-title":"J. Strength Cond. Res."},{"key":"ref_9","first-page":"100485","article-title":"Flexible electronics for cardiovascular healthcare monitoring","volume":"4","author":"Zhang","year":"2023","journal-title":"Innovation"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"137218","DOI":"10.1016\/j.neulet.2023.137218","article-title":"A study of trust behavior and its neural basis in athletes under long-term exercise training","volume":"805","author":"Wang","year":"2023","journal-title":"Neurosci. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hu, F., Wang, H., Feng, N., Zhou, B., Wei, C., Lu, Y., Qi, Y., Jia, X., Tang, H., and Gouda, M.A. (2021). A novel fusion strategy for locomotion activity recognition based on multimodal signals. Biomed. Signal Process. Control, 67.","DOI":"10.1016\/j.bspc.2021.102524"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102279","DOI":"10.1016\/j.mechatronics.2019.102279","article-title":"Neural-network-enhanced torque estimation control of a soft wearable exoskeleton for elbow assistance","volume":"63","author":"Wu","year":"2019","journal-title":"Mechatronics"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.bspc.2017.10.008","article-title":"A systematic review on fatigue analysis in triceps brachii using surface electromyography","volume":"40","author":"Hussain","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_14","unstructured":"Hu, X. (2020). 7-Voluntary intention-driven rehabilitation robots for the upper limb. Intelligent Biomechatronics in Neurorehabilitation, Academic Press."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tepe, C., and Erdim, M. (2022). Classification of surface electromyography and gyroscopic signals of finger gestures acquired by Myo armband using machine learning methods. Biomed. Signal Process. Control, 75.","DOI":"10.1016\/j.bspc.2022.103588"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100415","DOI":"10.1016\/j.imu.2020.100415","article-title":"Muscle and joint force dependence of scaling and skill level of athletes in high-speed overhead task: Musculoskeletal simulation study","volume":"20","author":"Barnamehei","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_17","unstructured":"Smerdov, A., Zhou, B., Lukowicz, P., and Somov, A. (2020). Collection and Validation of Psycophysiological Data from Professional and Amateur Players: A Multimodal eSports Dataset. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1016\/j.mehy.2012.08.025","article-title":"New role for astroglia in learning: Formation of muscle memory","volume":"79","author":"Hassanpoor","year":"2012","journal-title":"Med. Hypotheses"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.imu.2018.06.004","article-title":"Surface electromyography based method for computing muscle strength and fatigue of biceps brachii muscle and its clinical implementation","volume":"12","author":"Kuthe","year":"2018","journal-title":"Inform. Med. Unlocked"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, S.H., Lin, C.B., Chen, Y., Chen, W., Huang, T.S., and Hsu, C.Y. (2019). An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise. Sensors, 19.","DOI":"10.3390\/s19143108"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"39","DOI":"10.35882\/jeeemi.v5i1.278","article-title":"Effect of Muscle Fatigue on EMG Signal and Maximum Heart Rate for Pre and Post Physical Activity","volume":"5","author":"Caesaria","year":"2023","journal-title":"J. Electron. Electromed. Eng. Med. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"253","DOI":"10.35882\/jeeemi.v6i3.440","article-title":"Comparison of the Adaboost Method and the Extreme Learning Machine Method in Predicting Heart Failure","volume":"6","author":"Mubaarok","year":"2024","journal-title":"J. Electron. Electromed. Eng. Med. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.cmpb.2017.10.024","article-title":"Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms","volume":"154","author":"Karthick","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102566","DOI":"10.1016\/j.jelekin.2021.102566","article-title":"The influence of contraction type, prior performance of a maximal voluntary contraction and measurement duration on fine-wire EMG amplitude","volume":"59","author":"Reeves","year":"2021","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_25","unstructured":"Kopple, J.D., Massry, S.G., Kalantar-Zadeh, K., and Fouque, D. (2022). Chapter 53\u2014Nonnutritional and nonhormonal methods to affect muscle strength and physical performance. Nutritional Management of Renal Disease, Academic Press. [4th ed.]."},{"key":"ref_26","unstructured":"Segil, J. (2019). Chapter Three\u2014Sensors and Transducers. Handbook of Biomechatronics, Academic Press."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"046051","DOI":"10.1088\/1741-2552\/ac89b4","article-title":"Non-invasive dual attention TCN for electromyography and motion data fusion in lower limb ambulation prediction","volume":"19","author":"Zhou","year":"2022","journal-title":"J. Neural Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/S1050-6411(00)00027-4","article-title":"Development of recommendations for SEMG sensors and sensor placement procedures","volume":"10","author":"Hermens","year":"2000","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_29","unstructured":"Hermens, H., and Freriks, B. (2022, June 06). Recommendations for Sensor Locations in Arm or Hand Muscles. Available online: http:\/\/www.seniam.org\/."},{"key":"ref_30","unstructured":"Brown, L. (2022, June 06). Alternating Dumbbell Curl Tutorial. Available online: https:\/\/criticalbody.com\/alternating-dumbbell-curl\/."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/78.492552","article-title":"Determining the Initial States in Forward-Backward Filtering","volume":"44","author":"Gustafsson","year":"1996","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_32","first-page":"6","article-title":"An Onset Detection Method for Action Surface EIectromyography Based on SampIe Entropy","volume":"44","author":"Chen","year":"2016","journal-title":"Acta Electron. Sin."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol.-Heart Circ. Physiol."},{"key":"ref_34","unstructured":"N, P., Kautish, S., and Peng, S.L. (2021). Chapter 9\u2014An improved time-frequency method for efficient diagnosis of cardiac arrhythmias. Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics, Academic Press."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7420","DOI":"10.1016\/j.eswa.2012.01.102","article-title":"Feature reduction and selection for EMG signal classification","volume":"39","author":"Phinyomark","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"118282","DOI":"10.1016\/j.eswa.2022.118282","article-title":"sEMG time\u2013frequency features for hand movements classification","volume":"210","author":"Karheily","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Nazmi, N., Rahman, M.A.A., Yamamoto, S.I., Ahmad, S.A., Malarvili, M.B., Mazlan, S.A., and Zamzuri, H. (2017). Assessment on Stationarity of EMG Signals with Different Windows Size During Isotonic Contractions. Appl. Sci., 7.","DOI":"10.3390\/app7101050"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jero, S.E., Bharathi, K.D., Karthick, P.A., and Ramakrishnan, S. (2021). Muscle fatigue analysis in isometric contractions using geometric features of surface electromyography signals. Biomed. Signal Process. Control, 68.","DOI":"10.1016\/j.bspc.2021.102603"},{"key":"ref_39","unstructured":"Too, J. (2022, July 06). Electromyography (EMG) Feature Extraction Toolbox. Available online: https:\/\/github.com\/JingweiToo\/EMG-Feature-Extraction-Toolbox."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.csda.2012.11.004","article-title":"Assessing agreement with intraclass correlation coefficient and concordance correlation coefficient for data with repeated measures","volume":"60","author":"Chen","year":"2013","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.jclinepi.2004.08.012","article-title":"Intraclass correlation coefficient and outcome prevalence are associated in clustered binary data","volume":"58","author":"Gulliford","year":"2005","journal-title":"J. Clin. Epidemiol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.compeleceng.2017.04.003","article-title":"Pattern recognition of electromyography signals based on novel time domain features for amputees\u2019 limb motion classification","volume":"67","author":"Samuel","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1109\/LSP.2020.2988172","article-title":"Statistical Behavior of Teager-Kaiser Energy Operator in Presence of White Gaussian Noise","volume":"27","author":"Preaux","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_44","unstructured":"Phinyomark, A., Hirunviriya, S., Limsakul, C., and Phukpattaranont, P. (2010, January 19\u201321). Evaluation of EMG feature extraction for hand movement recognition based on Euclidean distance and standard deviation. Proceedings of the ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology, Chiang Mai, Thailand."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.bspc.2018.05.036","article-title":"Effect of threshold values on the combination of EMG time domain features: Surface versus intramuscular EMG","volume":"45","author":"Waris","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.measurement.2013.08.049","article-title":"An algorithm for improving the coefficient accuracy of wavelet packet analysis","volume":"47","author":"Jiao","year":"2014","journal-title":"Measurement"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.compeleceng.2018.07.026","article-title":"Muscle fatigue compensation of the electromyography signal for elbow joint angle estimation using adaptive feature","volume":"71","author":"Triwiyanto","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1080\/00401706.1999.10485676","article-title":"Applied Smoothing Techniques for Data Analysis","volume":"41","author":"Deveaux","year":"1999","journal-title":"Technometrics"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/5\/1355\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:40:38Z","timestamp":1760028038000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/5\/1355"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,22]]},"references-count":48,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["s25051355"],"URL":"https:\/\/doi.org\/10.3390\/s25051355","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,22]]}}}