Article Per Year (5 Year)
p-Index From 2021 - 2026
0.947
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Applied Power Engineering (IJAPE) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Robotics and Control Systems
Shair, Ezreen Farina
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : international journal of robotics and control systems

 1 
Comparative Analysis of 1D – CNN, GRU, and LSTM for Classifying Step Duration in Elderly and Adolescents Using Computer Vision Lee, Teng Hong; Shair, Ezreen Farina; Abdullah, Abdul Rahim; Rahman, Kazi Ashikur; Ali, Nursabillilah Mohd; Saharuddin, Nur Zawani; Nazmi, Nurhazimah
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1588

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.
Classifying Gait Disorder in Neurodegenerative Disorders Among Older Adults Using Machine Learning Rahman, Kazi Ashikur; Shair, Ezreen Farina; Abdullah, Abdul Rahim; Lee, Teng Hong; Ali, Nursabillilah Mohd; Zakaria, Muhammad Iqbal; Al Betar, Mohammed Azmi
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1722

Abstract

Gait 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.
Co-Authors Abdul Aziz, Nor Hidayati Abdullah, Abdul Rahim Abidin, Izham Zainal Ahmad Sobri Muda, Ahmad Sobri Al Betar, Mohammed Azmi Ali, Nursabillilah Mohd Ghani, Sharin Ab Hanafi, Ainain Nur Kandaya, Shaarmila Karis, Mohd Safirin Lee, Teng Hong Nasir, Amar Saufi Nazmi, Nurhazimah Nur Zawani Saharuddin Rahman, Kazi Ashikur Saad, Norhashimah Mohd Sabri, Muhammad Izzat Ahmad Shamsudin, Nur Hazahsha Zakaria, Muhammad Iqbal