{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T18:52:55Z","timestamp":1793731975915,"version":"4.6.1"},"reference-count":28,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["Research Impact Fund (Ref. no.: R5028-20F)"],"award-info":[{"award-number":["Research Impact Fund (Ref. no.: R5028-20F)"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Markerless motion capture (MMC) technology has emerged as a clinical tool to assess the physical performance of patients. This study evaluates: (a) differences in upper limb joint angles between stroke survivors with different functional levels and their healthy counterparts in controlled indoor and uncontrolled outdoor environments; and (b) the relationship between the kinematic information obtained by the MMC system and the scores of manual motor assessments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A customized MMC system using an iPad Pro captured the participants\u2019 movements. Stroke survivors underwent three upper limb assessments and performed seven sets of upper limb tasks with their non-affected side, followed by their affected side. Healthy participants performed the same tasks with their dominant and non-dominant sides. The sensitivity and specificity of the machine models were calculated for classifying upper limb motor function levels using kinematic data from the MMC system.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Fifty stroke survivors and 49 healthy adults were recruited. Significant differences were found between the affected and non-affected sides of stroke participants in most tasks. Significant positive correlations were found between the results of the manual motor assessments and most of the kinematic parameters. The results of the four selected machine learning models revealed \u22650.85 sensitivity in the stroke upper limb functional level classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The MMC system and machine learning algorithms provide accurate data for evaluating upper limb recovery in stroke survivors. Further research is needed to explore the use of the MMC system by stroke survivors at home during remote therapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076251342009","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T03:36:17Z","timestamp":1750304177000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Upper limb kinematic measurement using markerless motion capturing (MMC) in stroke survivors: A cross-sectional experimental study"],"prefix":"10.1177","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9542-1841","authenticated-orcid":false,"given":"Winnie WT","family":"Lam","sequence":"first","affiliation":[{"name":"Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5909-4847","authenticated-orcid":false,"given":"Kenneth NK","family":"Fong","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5532-1018","authenticated-orcid":false,"given":"Chi-Wen","family":"Chien","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,6,19]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-1424-6_33"},{"key":"e_1_3_3_3_2","first-page":"1","article-title":"Assessments of motor function post stroke: a review","volume":"19","author":"Poole JL","year":"2001","unstructured":"Poole JL, Whitney SL. 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Prism vote: a stratified statistical framework to perform prediction for complex diseases. Hong Kong: The Chinese University of Hong Kong, 2020."},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.3138\/ptc.2011-03"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci13010003"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1177\/15459683231184186"},{"key":"e_1_3_3_26_2","first-page":"17","article-title":"Machine learning in safety and health research: a scientometric analysis","volume":"21","author":"Abdullah KH","year":"2023","unstructured":"Abdullah KH, Sofyan D. Machine learning in safety and health research: a scientometric analysis. 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