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Integrating visual and sensor data, the model enhances action recognition accuracy through multi-modal data fusion and provides personalized correction recommendations based on an adaptive feedback mechanism linked to user status. Experimental results demonstrate that AMMF-RC achieves significantly higher recognition accuracy across various fitness actions compared to single-modality and traditional multi-modal models, with a considerable reduction in feedback latency, effectively enhancing user training experiences. AMMF-RC not only expands the application scope of fitness guidance systems but also offers a viable reference framework for future intelligent fitness system development. <\/jats:p>","DOI":"10.1142\/s0218126625502421","type":"journal-article","created":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T00:11:51Z","timestamp":1739578311000},"source":"Crossref","is-referenced-by-count":0,"title":["AMMF-RC: An Adaptive Multi-Modal Model for Real-Time Fitness Motion Recognition and Personalized Correction"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9258-5708","authenticated-orcid":false,"given":"Lijian","family":"Liao","sequence":"first","affiliation":[{"name":"College of Physical Education and Health, Huaihua University, Huaihua 418000, P. R. 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