Using a deep learning method and data from two-dimensional (2D) marker-less video-based images for walking speed classification
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 measure...
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my.ump.umpir.316962021-07-26T14:05:10Z http://umpir.ump.edu.my/id/eprint/31696/ Using a deep learning method and data from two-dimensional (2D) marker-less video-based images for walking speed classification Sikandar, Tasriva Rabbi, Mohammad F. Kamarul Hawari, Ghazali Altwijri, Omar Alqahtani, Mahdi Almijalli, Mohammed Altayyar, Saleh Ahamed, Nizam U. QC Physics TK Electrical engineering. Electronics Nuclear engineering 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. MDPI 2021-04-02 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/31696/1/Using%20a%20deep%20learning%20method%20and%20data%20from%20two.pdf 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 https://doi.org/10.3390/s21082836 https://doi.org/10.3390/s21082836 |
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QC Physics TK Electrical engineering. Electronics Nuclear engineering Sikandar, Tasriva Rabbi, Mohammad F. Kamarul Hawari, Ghazali Altwijri, Omar Alqahtani, Mahdi Almijalli, Mohammed Altayyar, Saleh Ahamed, Nizam U. Using a deep learning method and data from two-dimensional (2D) marker-less video-based images for walking speed classification |
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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. |
format |
Article |
author |
Sikandar, Tasriva Rabbi, Mohammad F. Kamarul Hawari, Ghazali Altwijri, Omar Alqahtani, Mahdi Almijalli, Mohammed Altayyar, Saleh Ahamed, Nizam U. |
author_facet |
Sikandar, Tasriva Rabbi, Mohammad F. Kamarul Hawari, Ghazali Altwijri, Omar Alqahtani, Mahdi Almijalli, Mohammed Altayyar, Saleh Ahamed, Nizam U. |
author_sort |
Sikandar, Tasriva |
title |
Using a deep learning method and data from two-dimensional (2D) marker-less video-based images for walking speed classification |
title_short |
Using a deep learning method and data from two-dimensional (2D) marker-less video-based images for walking speed classification |
title_full |
Using a deep learning method and data from two-dimensional (2D) marker-less video-based images for walking speed classification |
title_fullStr |
Using a deep learning method and data from two-dimensional (2D) marker-less video-based images for walking speed classification |
title_full_unstemmed |
Using a deep learning method and data from two-dimensional (2D) marker-less video-based images for walking speed classification |
title_sort |
using a deep learning method and data from two-dimensional (2d) marker-less video-based images for walking speed classification |
publisher |
MDPI |
publishDate |
2021 |
url |
http://umpir.ump.edu.my/id/eprint/31696/1/Using%20a%20deep%20learning%20method%20and%20data%20from%20two.pdf http://umpir.ump.edu.my/id/eprint/31696/ https://doi.org/10.3390/s21082836 https://doi.org/10.3390/s21082836 |
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1706957257509437440 |
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13.160551 |