Comparative analysis of 1D – CNN, GRU, and LSTM for classifying step duration in elderly and adolescents using computer vision

Shair, Ezreen Farina and Lee, Teng Hong and Abdullah, Abdul Rahim and Rahman, Kazi Ashikur and Mohd Ali, Nursabillilah and Saharuddin, Nur Zawani and Nazmi, Nurhazimah (2025) Comparative analysis of 1D – CNN, GRU, and LSTM for classifying step duration in elderly and adolescents using computer vision. International Journal of Robotics and Control Systems, 5 (1). pp. 426-439. ISSN 2775-2658

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Abstract

Developing 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.

Item Type:	Article
Uncontrolled Keywords:	Computer vision, Step duration, Deep learning, Feature extraction, Time series data
Divisions:	Faculty Of Electrical Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	28 Oct 2025 01:10
Last Modified:	28 Oct 2025 01:10
URI:	http://eprints.utem.edu.my/id/eprint/29070
Statistic Details:	View Download Statistic

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