{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T21:23:28Z","timestamp":1791408208940,"version":"4.3.3"},"reference-count":48,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"funder":[{"name":"Korea Government","award":["IITP-2025-RS-2024-00438056, 50% and 2022R1F1A1063134, 50%"],"award-info":[{"award-number":["IITP-2025-RS-2024-00438056, 50% and 2022R1F1A1063134, 50%"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>\n                    Recently, the number of cases of musculoskeletal and neurological disorders, such as knee osteoarthritis (KOA) and Parkinson\u2019s disease (PD), has significantly increased. Numerous clinical methods have been proposed in research to diagnose these disorders; however, a current trend in diagnosis is through human gait patterns. Several researchers proposed different methods in this area, including gait detection utilizing sensor-based data and vision-based systems that include both marker-based and marker-free techniques. The majority of current studies are concerned with the classification of Parkinson\u2019s disease. Furthermore, many vision-based algorithms rely on human gait silhouettes or gait representations and employ traditional similarity-based methodologies. However, in this study, a novel approach is proposed in which spatiotemporal features are extracted\n                    <jats:italic>via<\/jats:italic>\n                    deep learning methods with a transfer learning paradigm. Following that, advanced deep learning approaches, including sequential models like gated recurrent unit (GRU), are used for additional analysis. The experimentation is performed on the publicly available KOA\u2013PD\u2013normal dataset comprising gait videos with various abnormalities, and the proposed model has the highest accuracy of approximately 94.81%.\n                  <\/jats:p>","DOI":"10.7717\/peerj-cs.2857","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T04:15:26Z","timestamp":1746504926000},"page":"e2857","source":"Crossref","is-referenced-by-count":6,"title":["Vision-based approach to knee osteoarthritis and Parkinson\u2019s disease detection utilizing human gait patterns"],"prefix":"10.7717","volume":"11","author":[{"given":"Zeeshan","family":"Ali","sequence":"first","affiliation":[{"name":"Research and Development Setups, National University of Computer and Emerging Sciences, Islamabad, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9524-5729","authenticated-orcid":true,"given":"Jihoon","family":"Moon","sequence":"additional","affiliation":[{"name":"Department of Data Science, Duksung Women\u2019s University, Seoul, Republic of South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saira","family":"Gillani","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Computer Science, University of Central Punjab, Lahore, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3370-8331","authenticated-orcid":true,"given":"Sitara","family":"Afzal","sequence":"additional","affiliation":[{"name":"Department of Software, Sejong University, Seoul, Republic of South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muazzam","family":"Maqsood","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Attock, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seungmin","family":"Rho","sequence":"additional","affiliation":[{"name":"Department of Industrial Security, Chung-Ang University, Seoul, Republic of South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2025,5,6]]},"reference":[{"issue":"5","key":"10.7717\/peerj-cs.2857\/ref-1","doi-asserted-by":"publisher","first-page":"e0175951","DOI":"10.1371\/journal.pone.0175951","article-title":"Vertical ground reaction force marker for Parkinson\u2019s disease","volume":"12","author":"Alam","year":"2017","journal-title":"PLOS 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