{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T13:59:59Z","timestamp":1762869599653,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T00:00:00Z","timestamp":1756080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Multimedia University\u2014Al-Zaytoonah University of Jordan Matching Grant","award":["MMUI\/240092"],"award-info":[{"award-number":["MMUI\/240092"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Parkinson\u2019s disease (PD) is a progressive neurodegenerative disorder that severely impacts motor function, necessitating early detection for effective management. However, current diagnostic methods are expensive and resource-intensive, limiting their accessibility. This study proposes a non-invasive, gait-based screening approach for PD using time-series analysis of video-derived motion data. Gait patterns indicative of PD are analyzed using videos containing walking sequences of PD subjects. The video data are processed via computer vision and human pose estimation techniques to extract key body points. Classification is performed using K-Nearest Neighbors (KNN) and Long Short-Term Memory (LSTM) networks in conjunction with time-series techniques, including Dynamic Time Warping (DTW), Bag of Patterns (BoP), and Symbolic Aggregate Approximation (SAX). KNN classifies based on similarity measures derived from these methods, while LSTM captures complex temporal dependencies. Additionally, Shapelet-based Classification is independently explored for its ability to serve as a self-contained classifier by extracting discriminative motion patterns. On a self-collected dataset (43 instances: 8 PD and 35 healthy), DTW-based classification achieved 88.89% accuracy for both KNN and LSTM. On an external dataset (294 instances: 150 healthy and 144 PD with varying severity), KNN and LSTM achieved 71.19% and 57.63% accuracy, respectively. The proposed approach enhances PD detection through a cost-effective, non-invasive methodology, supporting early diagnosis and disease monitoring. By integrating machine learning with clinical insights, this study demonstrates the potential of AI-driven solutions in advancing PD screening and management.<\/jats:p>","DOI":"10.3390\/sym17091385","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T06:25:57Z","timestamp":1756189557000},"page":"1385","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Non-Invasive Gait-Based Screening Approach for Parkinson\u2019s Disease Using Time-Series Analysis"],"prefix":"10.3390","volume":"17","author":[{"given":"Hui","family":"Chen","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0901-3831","authenticated-orcid":false,"given":"Tee","family":"Connie","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia"},{"name":"Center for Image and Vision Computing, COE for Artificial Intelligence, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent Wei Sheng","family":"Tan","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9217-6390","authenticated-orcid":false,"given":"Michael Kah Ong","family":"Goh","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia"},{"name":"Center for Image and Vision Computing, COE for Artificial Intelligence, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6634-7934","authenticated-orcid":false,"given":"Nor Izzati","family":"Saedon","sequence":"additional","affiliation":[{"name":"Department of Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad","family":"Al-Khatib","sequence":"additional","affiliation":[{"name":"Faculty of Science and Information Technology, Al-Zaytoonah University of Jordan, St. 594, Airport Rd., Amman 11733, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9010-6989","authenticated-orcid":false,"given":"Mahmoud","family":"Farfoura","sequence":"additional","affiliation":[{"name":"Faculty of Science and Information Technology, Al-Zaytoonah University of Jordan, St. 594, Airport Rd., Amman 11733, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2129","DOI":"10.1002\/mds.22340","article-title":"Movement Disorder Society-sponsored Revision of the Unified Parkinson\u2019s Disease Rating Scale (MDS-UPDRS): Scale Presentation and Clinimetric Testing Results","volume":"23","author":"Goetz","year":"2008","journal-title":"Mov. 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