Sikandar, Tasriva and Rahman, Sam Matiur and Islam, Dilshad and Ali, Md Asraf and Mamun, Md Abdullah Al and Rabbi, Mohammad Fazle and Kamarul Hawari, Ghazali and Altwijri, Omar and Almijalli, Mohammed and Ahamed, Nizam Uddin (2022) Walking speed classification from marker-free video images in two-dimension using optimum data and a deep learning method. Bioengineering, 9 (715). pp. 1-13. ISSN 2306-5354. (Published)
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Abstract
Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | bi-LSTM; Deep learning; Marker-free video; Optimal feature; Ratio-based body measurement; Redundant feature; Two-dimensional (2D) image; Walking speed; Walking speed classification |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 30 Apr 2024 06:32 |
| Last Modified: | 30 Apr 2024 06:32 |
| URI: | http://umpir.ump.edu.my/id/eprint/40599 |
| Download Statistic: | View Download Statistics |
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