Citation

Chen, Hui and Tee, Connie and Tan, Vincent Wei Sheng and Goh, Michael Kah Ong and Saedon, Nor ‘Izzati and Al- Khatib, Ahmad and Farfoura, Mahmoud (2025) A Non-Invasive Gait-Based Screening Approach for Parkinson’s Disease Using Time-Series Analysis. Symmetry, 17. p. 26. ISSN 2073-8994

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Abstract

Parkinson’s 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

Item Type:	Article
Uncontrolled Keywords:	Computer vision, human pose estimation, machine learning, Parkinson’s disease, time-series analysis
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science R Medicine > RC Internal medicine
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	04 Nov 2025 08:08
Last Modified:	06 Nov 2025 13:57
URII:	http://shdl.mmu.edu.my/id/eprint/14690

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