Review article
https://doi.org/10.26332/seemedj.v7i1.276
Computer Vision Solutions for Range of Motion Assessment
Jelena Aleksić
orcid.org/0009-0000-8914-4825
; University of Belgrade, Faculty of Sport and Physical Education, The Research Centre, Belgrade, Serbia
Abstract
Joint range of motion (ROM) is an important indicator of physical functionality and musculoskeletal health. In sports, athletes require adequate levels of joint mobility to minimize the risk of injuries and maximize performance, while in rehabilitation, restoring joint ROM is essential for faster recovery and improved physical function. Traditional methods for measuring ROM include goniometry, inclinometry and visual estimation; all of which are limited in accuracy due to the subjective nature of the assessment. With the rapid development of technology, new systems based on computer vision are continuously introduced as a possible solution for more objective and accurate measurements of the range of motion. Therefore, this article aimed to evaluate novel computer vision-based systems based on their accuracy and practical applicability for a range of motion assessment. The review covers a variety of systems, including motion-capture systems (2D and 3D cameras), RGB-Depth cameras, commercial software systems and smartphone apps. Furthermore, this article also highlights the potential limitations of these systems and explores their potential future applications in sports and rehabilitation.
Keywords
joint range of motion; rehabilitation; computer vision system; machine learning; motion capture
Hrčak ID:
301087
URI
Publication date:
30.4.2023.
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