(Universiti Teknikal Malaysia Melaka, Malaysia)(2) * Ezreen Farina Shair
(Universiti Teknikal Malaysia Melaka, Malaysia)(3) Abdul Rahim Abdullah
(Universiti Teknikal Malaysia Melaka, Malaysia)(4) Kazi Ashikur Rahman
(Universiti Teknikal Malaysia Melaka, Malaysia)(5) Nursabillilah Mohd Ali
(Universiti Teknikal Malaysia Melaka, Malaysia)(6) Nur Zawani Saharuddin
(Universiti Teknikal Malaysia Melaka, Malaysia)(7) Nurhazimah Nazmi
(Universiti Teknologi Malaysia, Malaysia)*corresponding author
AbstractDeveloping a classification system that can predict the onset of neurodegenerative diseases or gait-related disorders in elders is vital for preventing incidents like falls. Early detection allows reduction in symptoms and treatment cost for the elderly. In this study, step duration data from five healthy adolescents with age range of 23 – 29 years old and five healthy elderly individuals with age range of 71 – 77 years old were sourced from PhysioNet. To ensure proper training of the deep learning models, synthetic data was generated from the original dataset using a noise jittering technique with random noise of a range between -0.01 and 0.01 added to the original data. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and 1D Convolutional Neural Network (1D-CNN) are used for training the data since the data is available in the form time series data. LSTM and GRU are advanced forms of Recurrent Neural Network (RNN) while 1D – CNN can capture temporal dependencies in sequential data. 1D – CNN has the advantages over GRU and LSTM of being more robust to noise and can capture complex patterns behind the data. These methods will be compared in terms of processing time and accuracy. Results show that 1D – CNN outperforms both LSTM and GRU with accuracy of 1.000 in less than 60 seconds. The novelty and contribution of this research shows that healthy old people and healthy young people can be classified with deep learning. Further direction of the research can explore the deep learning in classification of Parkinson’s disease.
KeywordsComputer Vision; Step Duration; Deep Learning; Feature Extraction; Time Series Data
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DOIhttps://doi.org/10.31763/ijrcs.v5i1.1588 |
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