{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T01:08:31Z","timestamp":1793236111312,"version":"4.5.0"},"reference-count":121,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Sci."],"abstract":"<jats:p>Markerless Human Pose Estimation (HPE) proved its potential to support decision making and assessment in many fields of application. HPE is often preferred to traditional marker-based Motion Capture systems due to the ease of setup, portability, and affordable cost of the technology. However, the exploitation of HPE in biomedical applications is still under investigation. This review aims to provide an overview of current biomedical applications of HPE. In this paper, we examine the main features of HPE approaches and discuss whether or not those features are of interest to biomedical applications. We also identify those areas where HPE is already in use and present peculiarities and trends followed by researchers and practitioners. We include here 25 approaches to HPE and more than 40 studies of HPE applied to motor development assessment, neuromuscolar rehabilitation, and gait &amp;amp; posture analysis. We conclude that markerless HPE offers great potential for extending diagnosis and rehabilitation outside hospitals and clinics, toward the paradigm of <jats:italic>remote medical care<\/jats:italic>.<\/jats:p>","DOI":"10.3389\/fcomp.2023.1153160","type":"journal-article","created":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T22:23:06Z","timestamp":1688682186000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":68,"title":["Markerless human pose estimation for biomedical applications: a survey"],"prefix":"10.3389","volume":"5","author":[{"given":"Andrea","family":"Avogaro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federico","family":"Cunico","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bodo","family":"Rosenhahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Setti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,7,6]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e042147","DOI":"10.1136\/bmjopen-2020-042147","article-title":"In-motion-app for remote general movement assessment: a multi-site observational study","volume":"11","author":"Adde","year":"2021","journal-title":"BMJ Open"},{"key":"B2","first-page":"1446","article-title":"\u201cPose-conditioned joint angle limits for 3d human pose reconstruction,\u201d","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Akhter","year":"2015"},{"key":"B3","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1007\/s11910-020-01049-z","article-title":"Machine learning for 3d kinematic analysis of movements in neurorehabilitation","volume":"20","author":"Arac","year":"2020","journal-title":"Curr. 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