{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T05:07:54Z","timestamp":1778735274185,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T00:00:00Z","timestamp":1777075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Support for Pioneering Research Initiated by the Next Generation (SPRING) program","award":["JPMJSP2108"],"award-info":[{"award-number":["JPMJSP2108"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Micromachines"],"abstract":"<jats:p>The Timed Up and Go (TUG) test is a clinical gold standard for assessing elderly mobility, yet its automated deployment in home-monitoring and resource-limited areas is hindered by high hardware costs and expert calibration requirements. This study introduces a Physics\u2013Data Hybrid framework specifically designed for uncalibrated consumer-grade CMOS cameras, enabling a \u201cplug-and-play\u201d solution for early Parkinson\u2019s disease (PD) risk screening. The proposed pipeline integrates learning-based pose perception with a self-evolving physics model to recover absolute metric-scale motion without manual checkerboard calibration. A noise-adaptive fusion strategy is implemented to reconcile 2D pixel dynamics with 3D kinematic consistency, overcoming the inherent scale ambiguity of monocular vision. Crucially, this framework enables the extraction of high-dimensional spatiotemporal parameters\u2014such as stride length coefficient of variation and mean gait velocity\u2014which provide a finer diagnostic resolution for capturing subtle motor fluctuations than conventional timing-only systems. Results from our pilot study with a cohort of 10 subjects demonstrate that these extracted metric features serve as decisive markers for risk staging simulated by dual-task-induced cognitive-motor-interference, achieving 98% screening accuracy and an overall classification accuracy of 87.32%. This framework provides a robust, low-cost tool for ubiquitous telehealth, potentially supporting early PD risk assessment in underserved populations.<\/jats:p>","DOI":"10.3390\/mi17050523","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T07:43:51Z","timestamp":1777448631000},"page":"523","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Physics\u2013Data Hybrid Framework Using Uncalibrated Consumer CMOS Vision: Pilot Study on Monocular Automatic TUG Assessment Towards Early Parkinson\u2019s Disease Risk Screening"],"prefix":"10.3390","volume":"17","author":[{"given":"Yuxiang","family":"Qiu","sequence":"first","affiliation":[{"name":"Department of Precision Engineering, Graduate School of Engineering, University of Tokyo, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Precision Engineering, Graduate School of Engineering, University of Tokyo, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Precision Engineering, Graduate School of Engineering, University of Tokyo, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1119-2223","authenticated-orcid":false,"given":"Jarred","family":"Fastier-Wooller","sequence":"additional","affiliation":[{"name":"Department of Precision Engineering, Graduate School of Engineering, University of Tokyo, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7094-227X","authenticated-orcid":false,"given":"Shun","family":"Muramatsu","sequence":"additional","affiliation":[{"name":"Department of Precision Engineering, Graduate School of Engineering, University of Tokyo, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6542-2800","authenticated-orcid":false,"given":"Michitaka","family":"Yamamoto","sequence":"additional","affiliation":[{"name":"Department of Precision Engineering, Graduate School of Engineering, University of Tokyo, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toshihiro","family":"Itoh","sequence":"additional","affiliation":[{"name":"Department of Precision Engineering, Graduate School of Engineering, University of Tokyo, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,25]]},"reference":[{"key":"ref_1","unstructured":"GBD 2016 Dementia Collaborators (2019). 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