(Universiti Teknikal Malaysia Melaka, Malaysia)(2) * Ezreen Farina Shair
(Universiti Teknikal Malaysia Melaka, Malaysia)(3) Abdul Rahim Abdullah
(Universiti Teknikal Malaysia Melaka, Malaysia)(4) Teng Hong Lee
(Universiti Teknikal Malaysia Melaka, Malaysia)(5) Nursabillilah Mohd Ali
(Universiti Teknikal Malaysia Melaka, Malaysia)(6) Muhammad Iqbal Zakaria
(Universiti Teknologi MARA, Malaysia)(7) Mohammed Azmi Al Betar
(Ajman University, United Arab Emirates)*corresponding author
AbstractGait disorders are a significant concern for older adults, particularly those with neurodegenerative diseases such as Parkinson’s disease, Huntington’s disease, and Amyotrophic Lateral Sclerosis. Accurately classifying these conditions using gait data remains a complex challenge, especially in older populations, due to age-related changes in gait patterns, comorbidities, and increased variability in mobility, which can obscure disease-specific characteristics. This study explicitly classifies neurodegenerative diseases in older adults by analysing age-specific gait force data. Continuous Wavelet Transform (CWT) was utilised for advanced feature extraction, capturing both temporal and spectral signal characteristics. Classifiers including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Multilayer Perceptron (MLP) were employed. The results demonstrated that SVM achieved an accuracy of 87.5%, outperforming RF and MLP, which achieved 83.3% and 50.0%, respectively. These findings underscore the importance of using tailored machine learning approaches to improve the diagnosis and management of neurodegenerative diseases in older adults. The potential for real-world application includes integration into clinical settings, enabling early detection and personalized interventions for individuals with gait disorders. KeywordsNeurodegenerative Disorders; Gait Analysis; Older Adults; Continuous Wavelet Transform; Machine Learning
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DOIhttps://doi.org/10.31763/ijrcs.v5i2.1722 |
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