{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T11:21:33Z","timestamp":1787570493228,"version":"build-2736575974"},"reference-count":35,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:00:00Z","timestamp":1787443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Electromyography (EMG)-based sensing is an important tool for assessing neuromuscular activity and monitoring athlete development; its reliability depends on electrode placement, signal quality, and accurate identification of muscle activation periods. This study proposes an intelligent EMG sensing framework integrating preliminary electrode placement assessment, muscle activity detection, feature extraction, and regression-based progression prediction. A placement assessment indicated that positioning the electrode adjacent to the innervation zone produced the highest RMS under the tested conditions. A two-stage activity detection method based on clustering and probabilistic modeling achieved an average error of 1.5% and a temporal deviation of 19 ms. Nine time-domain EMG features extracted from the detected activity segments were used to characterize athlete progression and estimate the time required to reach a reference neuromuscular profile. Among the methods, Linear Regression provided the best fit to the data, obtaining R2 = 0.987 and RMSE = 4.21 and suggesting a predominantly linear relationship between the EMG-derived features and training duration within the dataset. However, these results were obtained from only six longitudinal observation periods for a single representative athlete, with each period represented by a 90-dimensional EMG feature vector derived from the ten movement classes. Therefore, the results should be interpreted as preliminary, athlete-specific goodness-of-fit findings rather than evidence of generalizable predictive performance. Validation using larger longitudinal cohorts and independent datasets is required. The proposed framework is compatible with future IoT-enabled wearable and edge-computing architectures; however, hardware-level implementation was beyond the scope of this study.<\/jats:p>","DOI":"10.3390\/bios16090457","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T10:07:53Z","timestamp":1787566073000},"page":"457","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Intelligent Wearable EMG Sensing Framework for Athlete Neuromuscular Monitoring and Performance Progression Assessment"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0170-1098","authenticated-orcid":false,"given":"Kudratjon","family":"Zohirov","sequence":"first","affiliation":[{"name":"Software and Hardware Support of Computer Systems, Karshi State Technical University, Karshi 180100, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sardor","family":"Boykobilov","sequence":"additional","affiliation":[{"name":"Software and Hardware Support of Computer Systems, Karshi State Technical University, Karshi 180100, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gulmira","family":"Pardayeva","sequence":"additional","affiliation":[{"name":"Information Technologies, University of Information Technologies and Management, Karshi 180100, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nilufar","family":"Akhmedova","sequence":"additional","affiliation":[{"name":"Software and Hardware Support of Computer Systems, Karshi State Technical University, Karshi 180100, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dilobar","family":"Ilmurodova","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Technologies, Karshi State Technical University, Karshi 180100, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iroda","family":"Uralova","sequence":"additional","affiliation":[{"name":"Department of Informatics and Computer Graphics, Tashkent State Transport University, Tashkent 100167, Uzbekistan"},{"name":"Department of Convergency of Digital Technologies, Tashkent University of Information Technologies Named After Muhammad al-Khwarizmi, Tashkent 100084, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zavqiddin","family":"Temirov","sequence":"additional","affiliation":[{"name":"Department of Digital Technologies, Alfraganus University, Tashkent 100190, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1988-5376","authenticated-orcid":false,"given":"Rashid","family":"Nasimov","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Leone, A., Carluccio, A.M., Caroppo, A., Manni, A., and Rescio, G. 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