Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. (May 2023)

• Record Type: Journal Article
• Authors: Sardari, Sara; Sharifzadeh, Sara; Daneshkhah, Alireza; Nakisa, Bahareh; Loke, Seng W.; Palade, Vasile; Duncan, Michael J.
• Is Part Of: Computers in biology and medicine. Volume 158(2023)
• Publication Date: 2023-05
• Subjects: Activity evaluation -- Activity recognition -- Computer vision -- Deep learning -- Physical rehabilitation -- Skeleton data; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106835
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Highlights: A review of human activity evaluation using skeleton data for rehabilitation. Limitations of previous studies and possible future studies are discussed. Discussion for automatic learning strategies and evaluation methods is given.