Analysis of gait patterns in neurodegenerative disorders among older adults: A ground reaction force data approach

Kazi Ashikur, Rahman and Shair, Ezreen Farina and Abdullah, Abdul Rahim and Lee, Teng Hong and Nazmi N. and Fahad N. (2024) Analysis of gait patterns in neurodegenerative disorders among older adults: A ground reaction force data approach. In: Proceedings of the 10th World Congress on Electrical Engineering and Computer Systems and Sciences (EECSS'24), 19 August 2024 through 21 August 2024, Barcelona, Spain.

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Abstract

Increasing awareness of walking-related issues leading to falls, particularly in older adults, has highlighted this important concern. Even though walking is a fundamental human movement, studying it is difficult because it involves intricate brain, nerve, and muscle coordination. Neurodegenerative disorders like Amyotrophic Lateral Sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD) are frequently associated with walking limitations, highlighting the critical need for precise diagnostic tools. This study employed a comprehensive approach, delving into the intricate examination of gait patterns in individuals with neurodegenerative disorders. We used ground reaction force (GRF) step data from the Physionet public database, which converted into the time-frequency domain using continuous wavelet transform (CWT). We applied feature extraction techniques to identify unique gait characteristics for each disorder. Our findings revealed significant differences in gait among neurodegenerative diseases, with Parkinson's disease exhibiting the highest variability, ALS showing less variability, and Huntington's disease falling in between. These results illustrate the complex nature of walking issues in neurodegenerative diseases, highlighting the necessity of specific diagnostic approaches.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Neurodegenerative disorders, Gait analysis, Older adults, Ground reaction force, Time-frequency
Divisions:	Faculty Of Electrical Technology And Engineering
Depositing User:	NUR FARISAH JAFRIN
Date Deposited:	23 Jul 2026 00:40
Last Modified:	23 Jul 2026 00:40
URI:	http://eprints.utem.edu.my/id/eprint/29887
Statistic Details:	View Download Statistic

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