{"id":10611,"date":"2026-08-22T16:57:37","date_gmt":"2026-08-22T08:57:37","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=10611"},"modified":"2026-08-22T18:02:34","modified_gmt":"2026-08-22T10:02:34","slug":"jase-202611-34-064","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-064","title":{"rendered":"Federated Learning-Based Sports Motion Recognition Integrating Smart Sports Equipment Condition Monitoring and Personal Privacy Protection Mechanism"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Xu Chu<\/i><\/a> <\/p>\n\n\n\n

Jilin Animation Institute, ChangChun, 130012, China <\/p>\n<\/div>\n\n\n\n

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Received: July 21, 2026
Accepted: August 14, 2026
Publication Date: August 22, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Privacy-Preserving Federated Training End-to-End Flow<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.064<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Wearable and edge-aware systems for sports motion recognition increasingly collect multi-source data (posture, acceleration, angular velocity) plus smart-equipment sensor signals (vibration, impact, temperature, wear). Centralized training exposes exercise habits and usage patterns while imposing high communication burden. This study proposes a scalable, privacy-preserving federated learning framework integrating motion recognition with equipment monitoring under non-IID distributions: edge devices preprocess and segment data via sliding window, train a lightweight CNN-BiGRU-Attention model locally, then prune, DP-perturb, and compress gradients before the server securely aggregates and redistributes the model to selected clients. Under non-IID conditions, FedAvg reaches 89.7% accuracy, FedProx 91.1%, and the proposed method 94.2%. Communication cost falls from 5.60 GB to 2.66 GB after 140 rounds (52.4% below FedAvg), and member-inference\/gradient inversion attack success rates fall to 15%\/12%.<\/p>\n\n\n\n

Keywords: <\/em>sports motion recognition, federated learning, smart sports equipment monitoring, wearable sensors, differential privacy, secure aggregation, communication compression<\/em><\/p>\n\n\n\n

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