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To take full advantage of this potential, the pipelines need to be validated against established clinical and electrophysiological gold standards. This study examines the validity of the Mediapipe (by Google) and Vision (by Apple) smartphone-enabled hand detection frameworks for tremor analysis. Both frameworks were tested in virtual experiments with simulated tremulous hands to determine the optimal camera position for hand tremor assessment and the minimum detectable tremor amplitude and frequency. Both frameworks were then compared with optical motion capture (OMC), accelerometry, and clinical ratings in 20 tremor patients. Both CV frameworks accurately measured tremor peak frequency. Significant correlations were found between CV-assessed tremor amplitudes and Essential Tremor Rating Assessment Scale (TETRAS) scores. However, the accuracy of amplitude estimation compared to OMC as ground truth was insufficient for clinical application. In conclusion, CV-based tremor analysis is an accurate and simple clinical assessment tool to determine tremor frequency. Further improvements in amplitude estimation are needed.<\/jats:p>","DOI":"10.1038\/s41598-025-97252-4","type":"journal-article","created":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T01:28:51Z","timestamp":1744939731000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Validity of tremor analysis using smartphone compatible computer vision frameworks"],"prefix":"10.1038","volume":"15","author":[{"given":"Robin","family":"Wolke","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julius","family":"Welzel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walter","family":"Maetzler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G\u00fcnther","family":"Deuschl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos","family":"Becktepe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,18]]},"reference":[{"key":"97252_CR1","doi-asserted-by":"publisher","first-page":"103854","DOI":"10.1016\/j.ridd.2021.103854","volume":"110","author":"N Silva","year":"2021","unstructured":"Silva, N. et al. The future of General Movement Assessment: The role of computer vision and machine learning\u2014A scoping review. Res. Dev. Disabil. 110, 103854. https:\/\/doi.org\/10.1016\/j.ridd.2021.103854 (2021).","journal-title":"Res. Dev. Disabil."},{"key":"97252_CR2","doi-asserted-by":"publisher","first-page":"120202","DOI":"10.1016\/j.jns.2022.120202","volume":"435","author":"RJ Elble","year":"2022","unstructured":"Elble, R. J. & Ondo, W. Tremor rating scales and laboratory tools for assessing tremor. J. Neurol. Sci. 435, 120202. https:\/\/doi.org\/10.1016\/j.jns.2022.120202 (2022).","journal-title":"J. Neurol. Sci."},{"issue":"1","key":"97252_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-024-01153-1","volume":"7","author":"MU Friedrich","year":"2024","unstructured":"Friedrich, M. U. et al. \u2018Validation and application of computer vision algorithms for video-based tremor analysis. NPJ Digit. 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