Using a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed Classification
| Autor: | Kamarul Hawari Ghazali, Mohammad Fazle Rabbi, Saleh S Altayyar, Omar Altwijri, Mahdi Alqahtani, Mohammed Almijalli, Nizam Uddin Ahamed, Tasriva Sikandar |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
2d images
Computer science Movement TP1-1185 02 engineering and technology Walking Biochemistry Article Analytical Chemistry rehabilitation 03 medical and health sciences 0302 clinical medicine gait impairment 0202 electrical engineering electronic engineering information engineering Humans Computer vision human mobility Aged care Electrical and Electronic Engineering Instrumentation Video based Gait Functional movement Aged business.industry Chemical technology Deep learning quasi-periodic pattern deep learning Atomic and Molecular Physics and Optics walking speed pattern Walking Speed Preferred walking speed Gait impairment marker-less video 020201 artificial intelligence & image processing Artificial intelligence business LSTM walking speed classification 030217 neurology & neurosurgery 2D image |
| Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 2836, p 2836 (2021) Sensors Volume 21 Issue 8 |
| ISSN: | 1424-8220 |
| Popis: | Human body measurement data related to walking can characterize functional movement 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. |
| Databáze: | OpenAIRE |
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