{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:12:27Z","timestamp":1789121547462,"version":"build-2803163510"},"reference-count":42,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen Science and Technology Program","award":["KCXFZ202307311093300001"],"award-info":[{"award-number":["KCXFZ202307311093300001"]}]},{"name":"Shenzhen Medical Research Fund","award":["A2303065"],"award-info":[{"award-number":["A2303065"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62363006"],"award-info":[{"award-number":["62363006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62363006"],"award-info":[{"award-number":["62363006"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Estimating joint torque in athletes provides important information for understanding neuromuscular performance and optimizing high-intensity training. While surface electromyography (sEMG) offers a non-invasive measure of muscle activation, existing methods often struggle to balance prediction accuracy, computational efficiency, and generalizability across different movement conditions. This study proposes a Hierarchical Three-Stage Feature Selection (H3FS) strategy for efficient and stable sEMG-based joint torque estimation. H3FS integrates domain knowledge, statistical and model-driven screening to generate compact, physiologically meaningful feature sets tailored to various application contexts. Using isokinetic movement data from the shoulder, elbow, and knee joints of 39 young athletes, H3FS identified the optimal configuration, ELATE-3, comprising logarithmic mean absolute value (LMAV), waveform length (WL), and Teager\u2013Kaiser energy operator (TKEO). The study also introduces an Integrated Performance Index (IPI) to evaluate prediction accuracy and computational cost. Results indicate that XGBoost with ELATE-3 achieves the best balance between accuracy and real-time performance (average R2 = 0.85, RMSE = 16.2, IPI = 0.013). It outperforms both traditional and data-driven feature sets, demonstrating strong generalization and low latency across joints, making it highly suitable for low-latency estimation on the adopted computational platform. The proposed strategy offers a systematic, scalable solution for joint torque estimation, supporting athletic training optimization and neuromuscular assessment, with potential future applications in rehabilitation monitoring.<\/jats:p>","DOI":"10.3390\/s26185768","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T09:09:40Z","timestamp":1789117780000},"page":"5768","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hierarchical Three-Stage Feature Selection Strategy for Efficient and Generalizable sEMG-Based Joint Torque Estimation in Athletic Training and Rehabilitation"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3678-6613","authenticated-orcid":false,"given":"Yufeng","family":"Zheng","sequence":"first","affiliation":[{"name":"The Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8102-122X","authenticated-orcid":false,"given":"Pingao","family":"Huang","sequence":"additional","affiliation":[{"name":"The School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2051-0192","authenticated-orcid":false,"given":"Hongqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"The School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7417-5733","authenticated-orcid":false,"given":"Yongxue","family":"Wang","sequence":"additional","affiliation":[{"name":"The College of Integrated Circuits, Shenzhen Polytechnic University, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0830-0011","authenticated-orcid":false,"given":"Chunlong","family":"Gan","sequence":"additional","affiliation":[{"name":"The Guizhou Institute of Sport Science (Guizhou Anti-Doping Agency), Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5312-0362","authenticated-orcid":false,"given":"Hui","family":"Wang","sequence":"additional","affiliation":[{"name":"The Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Al-Ayyad, M., Abu Owida, H., De Fazio, R., Al-Naami, B., and Visconti, P. (2023). Electromyography monitoring systems in rehabilitation: A review of clinical applications, wearable devices and signal acquisition methodologies. Electronics, 12.","DOI":"10.3390\/electronics12071520"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s11517-019-02073-z","article-title":"Evaluation of surface EMG-based recognition algorithms for decoding hand movements","volume":"58","author":"Abbaspour","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fraser, G.D., Chan, A.D.C., Green, J.R., and MacIsaac, D.T. (2013). Biosignal quality analysis of surface EMG using a correlation coefficient test for normality. Proceedings of the 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA), IEEE.","DOI":"10.1109\/MeMeA.2013.6549735"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jaramillo-Y\u00e1nez, A., Benalc\u00e1zar, M.E., and Mena-Maldonado, E. (2020). Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review. Sensors, 20.","DOI":"10.3390\/s20092467"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sun, J., Liu, G., Sun, Y., Lin, K., Zhou, Z., and Cai, J. (2022). Application of Surface Electromyography in Exercise Fatigue: A Review. Front. Syst. Neurosci., 16.","DOI":"10.3389\/fnsys.2022.893275"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Moin, A., Zhou, A., Benatti, S., Rahimi, A., Benini, L., and Rabaey, J.M. (2019). Analysis of contraction effort level in EMG-based gesture recognition using hyperdimensional computing. arXiv.","DOI":"10.1109\/BIOCAS.2019.8919214"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, L., Jiao, Z., Li, Y., and Chang, Y. (2025). Movement Recognition and Muscle Force Estimation of Wrist Based on Electromyographic Signals of Forearm. Biosensors, 15.","DOI":"10.3390\/bios15040259"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xu, B., Zhang, K., Yang, X., Liu, D., Hu, C., Li, H., and Song, A. (2022). Natural grasping movement recognition and force estimation using electromyography. Front. Neurosci., 16.","DOI":"10.3389\/fnins.2022.1020086"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhang, B., Wang, H., and Gouda, M.A. (2025). Surface Electromyography Monitoring of Muscle Changes in Male Basketball Players During Isotonic Training. Sensors, 25.","DOI":"10.3390\/s25051355"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/0013-4694(52)90008-4","article-title":"Relation of human electromyogram to muscular tension","volume":"4","author":"Inman","year":"1952","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.jelekin.2009.08.005","article-title":"Methodological aspects of SEMG recordings for force estimation\u2014A tutorial and review","volume":"20","author":"Staudenmann","year":"2010","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, K., Yuan, Y., Deng, Z., and Lui, Y. (2025). Parameter identification and sensitivity analysis of a lower-limb musculoskeletal model. Front. Bioeng. Biotechnol., 13.","DOI":"10.3389\/fbioe.2025.1566381"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Perera, C.K., Gopalai, A.A., Gouwanda, D., and Ahmad, S.A. (2025). A Focused Review on Upper and Lower Limb Joint Torque Estimation via Neural Networks. Pertanika J. Sci. Technol., 33.","DOI":"10.47836\/pjst.33.1.06"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ye, Y., Liu, C., Zemiti, N., and Yang, C. (2019). Optimal Feature Selection for EMG-Based Finger Force Estimation Using LightGBM Model. 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), IEEE.","DOI":"10.1109\/RO-MAN46459.2019.8956453"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104012","DOI":"10.1016\/j.bspc.2022.104012","article-title":"Bagged tree ensemble modelling with feature selection for isometric EMG-based force estimation","volume":"78","author":"Hajian","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1109\/TAI.2021.3066565","article-title":"Deep Neural Network Approach in EMG-Based Force Estimation for Human\u2013Robot Interaction","volume":"2","author":"Su","year":"2021","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.1109\/TNSRE.2024.3375320","article-title":"Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals","volume":"32","author":"Ma","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103550","DOI":"10.1016\/j.bspc.2022.103550","article-title":"Hand grip force estimation via EMG imaging","volume":"74","author":"Fialkoff","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1007\/s11517-021-02466-z","article-title":"Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques","volume":"60","author":"Mokri","year":"2022","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104174","DOI":"10.1016\/j.bspc.2022.104174","article-title":"A resistive force correlated electromyogram feature selection method for muscle strength prediction","volume":"79","author":"Saranya","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102864","DOI":"10.1016\/j.jelekin.2024.102864","article-title":"Evaluation of generic EMG-Torque models across two Upper-Limb joints","volume":"75","author":"Wang","year":"2024","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_22","unstructured":"Phinyomark, A., Limsakul, C., and Phukpattaranont, P. (2009). A novel feature extraction for robust EMG pattern recognition. arXiv."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"27","DOI":"10.5755\/j01.eee.122.6.1816","article-title":"Feature Extraction and Reduction of Wavelet Transform Coefficients for EMG Pattern Classification","volume":"122","author":"Phinyomark","year":"2012","journal-title":"Elektron. Elektrotech."},{"key":"ref_25","unstructured":"Hudgins, B., Parker, P., and Scott, R.N. (1991). The Recognition of Myoelectric Patterns for Prosthetic Limb Control. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991, IEEE."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/72.298224","article-title":"Using mutual information for selecting features in supervised neural net learning","volume":"5","author":"Battiti","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_27","unstructured":"Duda, R.O., Hart, P.E., and Stork, D.G. (2001). Pattern Classification, Wiley-Interscience. [2nd ed.]."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression Shrinkage and Selection Via the Lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/0167-8655(89)90037-8","article-title":"A note on genetic algorithms for large-scale feature selection","volume":"10","author":"Siedlecki","year":"1989","journal-title":"Pattern Recognit. Lett."},{"key":"ref_31","unstructured":"Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, MIT Press."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1109\/ICASSP.1990.115702","article-title":"On a simple algorithm to calculate the \u2018energy\u2019 of a signal","volume":"Volume 1","author":"Kaiser","year":"1990","journal-title":"Proceedings of the International Conference on Acoustics, Speech, and Signal Processing"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_34","unstructured":"Witten, I.H., and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann. [2nd ed.]."},{"key":"ref_35","unstructured":"Kelley, H.H. (1961). The proof and measurement of association between two things. Studies in Individual Differences: The Search for Intelligence, Appleton-Century-Crofts."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3024","DOI":"10.1109\/78.277799","article-title":"Energy separation in signal modulations with application to speech analysis","volume":"41","author":"Maragos","year":"1993","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Aslam, S., Habyarimana, J.D.D., and Bin, S.Y. (2025). Neuromuscular adaptations to resistance training in elite versus recreational athletes. Front. Physiol., 16.","DOI":"10.3389\/fphys.2025.1598149"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.clinbiomech.2008.08.003","article-title":"Surface electromyography and muscle force: Limits in sEMG\u2013force relationship and new approaches for applications","volume":"24","author":"Rau","year":"2009","journal-title":"Clin. Biomech."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1007\/s10916-009-9292-7","article-title":"Muscle Fatigue Detection in EMG Using Time\u2013Frequency Methods, ICA and Neural Networks","volume":"34","author":"Subasi","year":"2010","journal-title":"J. Med. Syst."},{"key":"ref_40","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and Variable Selection Via the Elastic Net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning, Springer. [2nd ed.]. Chapter 3\u20134.","DOI":"10.1007\/978-0-387-84858-7"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5768\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T09:10:35Z","timestamp":1789117835000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5768"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,11]]},"references-count":42,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185768"],"URL":"https:\/\/doi.org\/10.3390\/s26185768","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,11]]}}}