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This technology takes advantage of human gaze features and binocular vision, which significantly improves the accuracy and reliability of motion analysis. By using dual cameras, the system can precisely calibrate the gaze direction, achieving more accurate gaze tracking. In addition, this framework conducts a comprehensive analysis of the inherent errors in the binocular gaze tracking algorithm, which achieves more accurate and robust motion detection. The intelligent motion analysis system built based on advanced image recognition technology further consolidates the capabilities of this technology. This system not only provides real\u2010time, high\u2010precision motion data but also has outstanding application potential in the industrial Internet of Things (IIoT) environment, as accurate motion understanding is crucial for optimizing human\u2010machine interaction and improving operational efficiency in such an environment. Moreover, the system's adaptability to dynamic conditions ensures consistent performance across various industrial settings.<\/jats:p>","DOI":"10.1002\/itl2.70187","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T02:30:23Z","timestamp":1765247423000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Industrial IoT\u2010Oriented Motion Analysis Based on Binocular Vision and Eye\u2010Tracking for Sports Scenarios"],"prefix":"10.1002","volume":"9","author":[{"given":"Yunna","family":"Liu","sequence":"first","affiliation":[{"name":"Shanghai Lixin University of Accounting and Finance  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaotong","family":"Liu","sequence":"additional","affiliation":[{"name":"WuHan Sports University  Wuhan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Ni","sequence":"additional","affiliation":[{"name":"Shanghai Lixin University of Accounting and Finance  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7942-7195","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai University of Traditional Chinese Medicine  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"e_1_2_6_2_1","unstructured":"J.RedmonandA.Farhadi \u201cYOLOv3: An Incremental Improvement \u201darXiv2018 arXiv:1804.02767 [cs.CV] https:\/\/doi.org\/10.48550\/arXiv.1804.02767."},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-59218-w"},{"issue":"2","key":"e_1_2_6_4_1","first-page":"145","article-title":"A Method Combining DaSiamRPN and HOG\u2010SVM for Athlete Detection and Tracking","volume":"6","author":"Sheng M.","year":"2020","journal-title":"Journal of Sports Analytics"},{"issue":"4","key":"e_1_2_6_5_1","first-page":"1645","article-title":"A Real\u2010Time Tracking Algorithm for Thermal Video From a Sports Game","volume":"23","author":"Gade R.","year":"2014","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15907-7_15"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.888718"},{"issue":"1","key":"e_1_2_6_8_1","first-page":"297","article-title":"Adaptive Neural Networks for Control of Movement","volume":"563","author":"Arbib M. 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