A review of computer vision-based approaches for physical rehabilitation and assessment

Abstract

The computer vision community has extensively researched the area of human motion analysis, which primarily focuses on pose estimation, activity recognition, pose or gesture recognition and so on. However for many applications, like monitoring of functional rehabilitation of patients with musculo skeletal or physical impairments, the requirement is to comparatively evaluate human motion. In this survey, we capture important literature on vision-based monitoring and physical rehabilitation that focuses on comparative evaluation of human motion during the past two decades and discuss the state of current research in this area. Unlike other reviews in this area, which are written from a clinical objective, this article presents research in this area from a computer vision application perspective. We propose our own taxonomy of computer vision-based rehabilitation and assessment research which are further divided into sub-categories to capture novelties of each research. The review discusses the challenges of this domain due to the wide ranging human motion abnormalities and difficulty in automatically assessing those abnormalities. Finally, suggestions on the future direction of research are offered.

Considerations for at-home upper-limb rehabilitation technology following stroke: Perspectives of stroke survivors and therapists

“… 23 Some success in identifying compensatory movements has been achieved using a combination of video and depth sensing. 43 , 44 A review of computer vision-based approaches to rehabilitation assessment 45 found that most approaches require both video and depth information, such as that provided by the Microsoft Kinect sensor; however, as deep learning methods improve it may become possible to achieve reasonable accuracy from video alone. It should be noted that requiring that a camera always be on when a patient is using their rehabilitation robot is less respectful of patient privacy.…”

The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review

“…Besides all these types of motion capture technologies, the recent developments in the computer vision field extended the use of low-cost RGB and depth sensors to perform motion analysis by the means of human pose estimation algorithms based on deep learning frameworks [33,30,42]. Human pose estimation is a field of computer vision that aims to predict the poses of human bodies by extracting joints from images and videos for motion analysis [65]. Contrarily to wearable sensors, AI-based human motion modeling enables commercial systems equipped with a camera and low-cost hardware, such as tablets and smartphones, to perform inexpensive and unobtrusive home-based monitoring in patients' daily life [33,35,36].…”

Comparative Study Between Machine learning algorithms and feature ranking techniques on UI-PRMD dataset

“…Numerous individuals all over the world are coping with the short-and long-term impacts of COVID-19 and often require rehabilitation to aid in their recovery. Numerous studies have been conducted on the topic of physical rehabilitation exercise classification [12] [13] [14] [15]. The Proposed system aims to reduce the requirement that the patient performs the exercises in front of a physical therapist present at all times.…”