Sikandar, Tasriva and Rabbi, Mohammad F. and Kamarul Hawari, Ghazali and Altwijri, Omar and Alqahtani, Mahdi and Almijalli, Mohammed and Altayyar, Saleh and Ahamed, Nizam U. (2021) Using a deep learning method and data from two-dimensional (2D) marker-less video-based images for walking speed classification. Sensors, 21 (8). pp. 1-16. ISSN 1424-8220. (Published)
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Abstract
Human body measurement data related to walking can characterize functional move ment and thereby become an important tool for health assessment. Single-camera-captured two dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | 2D image; Marker-less video; Walking speed pattern; Walking speed classification; Quasi-periodic pattern; LSTM; Deep learning; Rehabilitation; Human mobility; Gait impairment |
| Subjects: | Q Science > QC Physics T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 26 Jul 2021 14:05 |
| Last Modified: | 26 Jul 2021 14:05 |
| URI: | http://umpir.ump.edu.my/id/eprint/31696 |
| Download Statistic: | View Download Statistics |
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