{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T03:11:17Z","timestamp":1760411477033,"version":"build-2065373602"},"reference-count":28,"publisher":"World Scientific Pub Co Pte Ltd","issue":"14","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:p> Sports training is a systematic program designed to enhance an athlete\u2019s physical, technical, and mental skills. It involves various exercises, drills, and conditioning routines to boost performance. However, none of the existing works focused on analyzing the sudden changes in the acceleration of IoT sensors in sports people that fail to differentiate them as data anomalies. Sports training is a systematic program designed to enhance an athlete\u2019s physical, technical, and mental skills. It involves a variety of exercises, drills, and conditioning routines aimed at boosting overall performance. However, none of the existing works focused to analyze the sudden changes in the acceleration of IoT sensor in sportspersons that fails to differentiate as data anomaly. Therefore, this paper introduces an adaptive filtering-based sports training evaluation system in wireless sensor networks using SPJDNN.\u00a0At first, the sportspersons are initialized and their IoT data are sensed. The sensed data are then clustered using the N3GK-Means algorithm. Meanwhile, the sports training evaluation system is carried out based on the outcome of clustering. In this phase, the dataset is collected and then pre-processed. Afterward, from the pre-processed data, the dynamic time analysis, time\u2013frequency analysis and Wigner\u2013Ville Distribution plot are performed. Then, the features are extracted based on the outcomes of dynamic time analysis, time\u2013frequency analysis and Wigner\u2013Ville Distribution plot. Then, the dimensionality is reduced from the extracted features. Further, the jerks, outliers and deviations are analyzed from the dimensionality reduced data. Then, the classification is carried out using SPJDNN from the dimensionality reduced data and outcome of the performance of the sports. As per the experimental analysis, the presented approach attained 98.45% accuracy. <\/jats:p>","DOI":"10.1142\/s0218001425510140","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:11:24Z","timestamp":1749773484000},"source":"Crossref","is-referenced-by-count":0,"title":["An Adaptive Filtering-Based Sports Training Evaluation System in Wireless Sensor Networks Using SPJDNN"],"prefix":"10.1142","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2688-2868","authenticated-orcid":false,"given":"Li","family":"Hui","sequence":"first","affiliation":[{"name":"Hebi Polytechnic, Hebi, Henan 458030, P. R. 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