{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T19:23:01Z","timestamp":1790882581353,"version":"4.1.0"},"reference-count":86,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:00:00Z","timestamp":1782172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pediatric Reports"],"abstract":"<jats:p>Background: Artificial intelligence (AI)-driven markerless motion capture (MMC) technologies are increasingly being integrated into pediatric healthcare to improve the assessment and management of movement disorders. These video-based systems enable non-invasive motion analysis without wearable sensors, facilitating more natural movement assessment in children, particularly those with neurological or developmental conditions. Objectives: We evaluated the clinical applicability of AI-based MMC tools in pediatric settings for diagnosis, monitoring of motor development, and rehabilitation. Methods: This systematic review was registered in PROSPERO (CRD42024511787) and conducted by two independent reviewers, with a third reviewer resolving disagreements. The literature published between 2018 and 2025 was systematically searched. Studies involving pediatric populations or clinically relevant pediatric applications of MMC were included. Results: Of 1521 identified studies, 52 were finally selected. The included studies evaluated populations across a wide age range. However, seven of the included articles were specifically focused on underage populations. Infant studies primarily analyzed whole-body movements, emphasizing the relevance of global motor patterns in early development. OpenPose and AlphaPose were the most frequently used frameworks in pediatric research because of their automatic full-body key point detection, whereas DeepLabCut was commonly selected for its customizable labeling capabilities. Theia3D emerged as a promising clinically applicable solution with high accuracy. Most studies evaluated kinematic parameters as objective markers of motor performance and development. However, methodological heterogeneity and limited pediatric-specific validation remain important limitations. Conclusions: AI-driven MMC technologies show considerable potential to support objective, accessible, and child-friendly movement assessment in pediatric clinical practice.<\/jats:p>","DOI":"10.3390\/pediatric18040083","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:52:11Z","timestamp":1782453131000},"page":"83","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Markerless Motion Capture for Human Movement Estimation Using Artificial Intelligence: A Systematic Review"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5230-4418","authenticated-orcid":false,"given":"Georgina","family":"Dom\u00e8nech-Garcia","sequence":"first","affiliation":[{"name":"Department of Physiotherapy, Universitat Internacional de Catalunya (UIC), 08195 Barcelona, Spain"},{"name":"Fundaci\u00f3 Aspace Catalunya, 08038 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7653-6299","authenticated-orcid":false,"given":"Xavier","family":"Marimon","sequence":"additional","affiliation":[{"name":"Department of Strength of Materials and Structural Engineering, Universitat Polit\u00e8cnica de Catalunya (UPC-12 Barcelona TECH), 08028 Barcelona, Spain"},{"name":"Institute for Research and Innovation in Health (IRIS), 08028 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0906-9114","authenticated-orcid":false,"given":"Andoni","family":"Carrasco-Urribarren","sequence":"additional","affiliation":[{"name":"Department of Physiotherapy, Universitat Internacional de Catalunya (UIC), 08195 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0868-346X","authenticated-orcid":false,"given":"Alejandro E.","family":"Portela","sequence":"additional","affiliation":[{"name":"Bioengineering Institute of Technology, Universitat Internacional de Catalunya (UIC), 08195 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8637-5035","authenticated-orcid":false,"given":"Caritat","family":"Bagur-Calafat","sequence":"additional","affiliation":[{"name":"Department of Physiotherapy, Universitat Internacional de Catalunya (UIC), 08195 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,23]]},"reference":[{"key":"ref_1","unstructured":"McCarthy, J., Minsky, M.L., Rochester, N., and Shannon, C.E. 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