Walking speed classification from marker-free video images in two-dimension using optimum data and a deep learning method
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 intell...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/44922/ |
| _version_ | 1848827336742404096 |
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| author | Sikandar, Tasriva Rahman, Sam Matiur Islam, Dilshad Ali, Md. Asraf Al Mamun, Md. Abdullah Rabbi, Mohammad Fazle Kamarul Hawari, Ghazali Altwijri, Omar Almijalli, Mohammed Ahamed, Nizam U. |
| author_facet | Sikandar, Tasriva Rahman, Sam Matiur Islam, Dilshad Ali, Md. Asraf Al Mamun, Md. Abdullah Rabbi, Mohammad Fazle Kamarul Hawari, Ghazali Altwijri, Omar Almijalli, Mohammed Ahamed, Nizam U. |
| author_sort | Sikandar, Tasriva |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:59:06Z |
| format | Article |
| id | ump-44922 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:59:06Z |
| publishDate | 2022 |
| publisher | MDPI AG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-449222025-08-04T07:20:27Z https://umpir.ump.edu.my/id/eprint/44922/ Walking speed classification from marker-free video images in two-dimension using optimum data and a deep learning method Sikandar, Tasriva Rahman, Sam Matiur Islam, Dilshad Ali, Md. Asraf Al Mamun, Md. Abdullah Rabbi, Mohammad Fazle Kamarul Hawari, Ghazali Altwijri, Omar Almijalli, Mohammed Ahamed, Nizam U. QA Mathematics TK Electrical engineering. Electronics Nuclear engineering 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. MDPI AG 2022 Article PeerReviewed pdf en cc_by_4 https://umpir.ump.edu.my/id/eprint/44922/1/Walking%20speed%20classification%20from%20marker-free%20video%20images.pdf Sikandar, Tasriva and Rahman, Sam Matiur and Islam, Dilshad and Ali, Md. Asraf and Al Mamun, Md. Abdullah and Rabbi, Mohammad Fazle and Kamarul Hawari, Ghazali and Altwijri, Omar and Almijalli, Mohammed and Ahamed, Nizam U. (2022) Walking speed classification from marker-free video images in two-dimension using optimum data and a deep learning method. Bioengineering, 9 (11). pp. 1-13. ISSN 2306-5354. (Published) https://doi.org/10.3390/bioengineering9110715 https://doi.org/10.3390/bioengineering9110715 https://doi.org/10.3390/bioengineering9110715 |
| spellingShingle | QA Mathematics TK Electrical engineering. Electronics Nuclear engineering Sikandar, Tasriva Rahman, Sam Matiur Islam, Dilshad Ali, Md. Asraf Al Mamun, Md. Abdullah Rabbi, Mohammad Fazle Kamarul Hawari, Ghazali Altwijri, Omar Almijalli, Mohammed Ahamed, Nizam U. Walking speed classification from marker-free video images in two-dimension using optimum data and a deep learning method |
| title | Walking speed classification from marker-free video images in two-dimension using optimum data and a deep learning method |
| title_full | Walking speed classification from marker-free video images in two-dimension using optimum data and a deep learning method |
| title_fullStr | Walking speed classification from marker-free video images in two-dimension using optimum data and a deep learning method |
| title_full_unstemmed | Walking speed classification from marker-free video images in two-dimension using optimum data and a deep learning method |
| title_short | Walking speed classification from marker-free video images in two-dimension using optimum data and a deep learning method |
| title_sort | walking speed classification from marker-free video images in two-dimension using optimum data and a deep learning method |
| topic | QA Mathematics TK Electrical engineering. Electronics Nuclear engineering |
| url | https://umpir.ump.edu.my/id/eprint/44922/ https://umpir.ump.edu.my/id/eprint/44922/ https://umpir.ump.edu.my/id/eprint/44922/ |