Journal article 21780 views 111 downloads
Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review
,
Sara Sharifzadeh ,
Alireza Daneshkhah,
Bahareh Nakisa,
Seng W. Loke ,
Vasile Palade ,
Michael J. Duncan
Computers in Biology and Medicine, Volume: 158, Start page: 106835
Swansea University Author:
Sara Sharifzadeh
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DOI (Published version): 10.1016/j.compbiomed.2023.106835
Abstract
Performing prescribed physical exercises during home-based rehabilitation programs plays an important role in regaining muscle strength and improving balance for people with different physical disabilities. However, patients attending these programs are not able to assess their action performance in...
| Published in: | Computers in Biology and Medicine |
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| ISSN: | 0010-4825 |
| Published: |
Elsevier BV
2023
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa63069 |
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| Abstract: |
Performing prescribed physical exercises during home-based rehabilitation programs plays an important role in regaining muscle strength and improving balance for people with different physical disabilities. However, patients attending these programs are not able to assess their action performance in the absence of a medical expert. Recently, vision-based sensors have been deployed in the activity monitoring domain. They are capable of capturing accurate skeleton data. Furthermore, there have been significant advancements in Computer Vision (CV) and Deep Learning (DL) methodologies. These factors have promoted the solutions for designing automatic patient’s activity monitoring models. Then, improving such systems’ performance to assist patients and physiotherapists has attracted wide interest of the research community. This paper provides a comprehensive and up-to-date literature review on different stages of skeleton data acquisition processes for the aim of physio exercise monitoring. Then, the previously reported Artificial Intelligence (AI) - based methodologies for skeleton data analysis will be reviewed. In particular, feature learning from skeleton data, evaluation, and feedback generation for the purpose of rehabilitation monitoring will be studied. Furthermore, the associated challenges to these processes will be reviewed. Finally, the paper puts forward several suggestions for future research directions in this area. |
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| Keywords: |
Activity evaluation; Activity recognition; Computer vision; Deep learning; Physical rehabilitation; Skeleton data. |
| College: |
Faculty of Science and Engineering |
| Start Page: |
106835 |