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The framework combines MediaPipe-based joint tracking, unsupervised LSTM-autoencoder modeling, and targeted preprocessing to address clinical video noise. Trained only on normal gait, the model detects abnormal patterns in sarcopenia (SA) and Parkinson\u2019s disease (PD) patients, achieving detection rates of 97% and 88%, respectively. Our method achieves state-of-the-art performance in sarcopenia detection, surpassing recent sensor-based approaches that require wearable devices or handcrafted features. Furthermore, to the best of our knowledge, this is the first unified markerless framework capable of identifying both sarcopenia and Parkinson\u2019s disease using a single video-based system. To improve input quality, we address three common sources of error\u2013frame imbalance, clothing interference, and background clutter\u2014using YOLO-based frame filtering and semantic segmentation. This increased usable gait data by up to 38%. Joint-level analysis identified the knees as the most responsive to gait abnormalities, enabling interpretable and localized assessments. 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Predicting multi-joint kinematics of the upper limb from EMG signals across varied loads with a physics-informed neural network (2023). arXiv:2312.09418."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-26169-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-26169-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-26169-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T23:01:44Z","timestamp":1765580504000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-26169-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,26]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["26169"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-26169-9","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-6585779\/v1","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,26]]},"assertion":[{"value":"3 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2025","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original online version of this Article was revised: The Acknowledgements section in the original version of this Article was incorrect. The Acknowledgements section now reads: \"This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2023R1A2C1006639).\" The original Article has been corrected.","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"42074"}}