{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T20:24:09Z","timestamp":1761164649636,"version":"build-2065373602"},"reference-count":7,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>In the context of the growing integration of Internet of Things (IoT) and edge intelligence into sports technology, the ability to accurately monitor athlete fatigue in real time has become increasingly important for performance optimization and injury prevention. This paper presents a novel fatigue detection framework that leverages physiological signal fusion and personalized activity recognition, optimized for resource\u2010constrained IoT devices using Tiny Machine Learning (TinyML) techniques. The proposed system combines inertial and heart rate signals collected from wearable devices and computes a lightweight, on\u2010device Physiological Fatigue Index (PFI), enhanced with personalized calibration and adaptive thresholding. To support deployment on ultra\u2010low\u2010power microcontrollers, we apply quantization, pruning, and model distillation, reducing memory footprint and energy consumption while preserving high accuracy. Experimental results on data collected from 12 athletes demonstrate the effectiveness of the approach, achieving 93.4% accuracy and 44\u2009mWh hourly power use, outperforming several state\u2010of\u2010the\u2010art TinyML and classical baselines. This work contributes a deployable, scalable, and privacy\u2010aware solution for continuous sports fatigue assessment in real\u2010world environments.<\/jats:p>","DOI":"10.1002\/itl2.70053","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T11:25:14Z","timestamp":1753183514000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Low\u2010Power Physiological Fatigue Monitoring via\n                    <scp>TinyML<\/scp>\n                    \u2010Enabled Wearables for Sports Evaluation"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5835-355X","authenticated-orcid":false,"given":"Yuqiu","family":"Zhang","sequence":"first","affiliation":[{"name":"Jilin Agricultural Science and Technology University  Jilin China"}]}],"member":"311","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3012440"},{"key":"e_1_2_6_3_1","doi-asserted-by":"crossref","unstructured":"W.Guo X.Sheng andX.Zhu \u201cAssessment of Muscle Fatigue by Simultaneous sEMG and NIRS: From the Perspective of Electrophysiology and Hemodynamics \u201d(2017) IEEE 33\u201336.","DOI":"10.1109\/NER.2017.8008285"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/s16101569"},{"key":"e_1_2_6_5_1","doi-asserted-by":"crossref","unstructured":"V.Sharma D.Pau andJ.Cano \u201cEfficient Tiny Machine Learning for Human Activity Recognition on Low\u2010Power Edge Devices \u201d(2024) IEEE 85\u201390.","DOI":"10.1109\/RTSI61910.2024.10761203"},{"key":"e_1_2_6_6_1","doi-asserted-by":"crossref","unstructured":"M.Zeng L. T.Nguyen B.Yu et al. \u201cConvolutional Neural Networks for Human Activity Recognition Using Mobile Sensors \u201d(2014) In: IEEE 197\u2013205.","DOI":"10.4108\/icst.mobicase.2014.257786"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964897.1964918"},{"key":"e_1_2_6_8_1","unstructured":"Y.Lu \u201cResource\u2010Efficient Deep Learning for Mobile Activity Recognition on Edge Devices \u201d(2025) Master's thesis. University of Twente."}],"container-title":["Internet Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70053","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T18:26:15Z","timestamp":1761071175000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/itl2.70053"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,22]]},"references-count":7,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1002\/itl2.70053"],"URL":"https:\/\/doi.org\/10.1002\/itl2.70053","archive":["Portico"],"relation":{},"ISSN":["2476-1508","2476-1508"],"issn-type":[{"type":"print","value":"2476-1508"},{"type":"electronic","value":"2476-1508"}],"subject":[],"published":{"date-parts":[[2025,7,22]]},"assertion":[{"value":"2025-04-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70053"}}