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Review
. 2021 Jul 5:10:11795727211022330.
doi: 10.1177/11795727211022330. eCollection 2021.

The Potential of Computer Vision-Based Marker-Less Human Motion Analysis for Rehabilitation

Thomas Hellsten  1 , Jonny Karlsson  2 , Muhammed Shamsuzzaman  2 , Göran Pulkkis  2
Affiliations
Review

The Potential of Computer Vision-Based Marker-Less Human Motion Analysis for Rehabilitation

Thomas Hellsten et al. Rehabil Process Outcome. .

Abstract

Background: Several factors, including the aging population and the recent corona pandemic, have increased the need for cost effective, easy-to-use and reliable telerehabilitation services. Computer vision-based marker-less human pose estimation is a promising variant of telerehabilitation and is currently an intensive research topic. It has attracted significant interest for detailed motion analysis, as it does not need arrangement of external fiducials while capturing motion data from images. This is promising for rehabilitation applications, as they enable analysis and supervision of clients' exercises and reduce clients' need for visiting physiotherapists in person. However, development of a marker-less motion analysis system with precise accuracy for joint identification, joint angle measurements and advanced motion analysis is an open challenge.

Objectives: The main objective of this paper is to provide a critical overview of recent computer vision-based marker-less human pose estimation systems and their applicability for rehabilitation application. An overview of some existing marker-less rehabilitation applications is also provided.

Methods: This paper presents a critical review of recent computer vision-based marker-less human pose estimation systems with focus on their provided joint localization accuracy in comparison to physiotherapy requirements and ease of use. The accuracy, in terms of the capability to measure the knee angle, is analysed using simulation.

Results: Current pose estimation systems use 2D, 3D, multiple and single view-based techniques. The most promising techniques from a physiotherapy point of view are 3D marker-less pose estimation based on a single view as these can perform advanced motion analysis of the human body while only requiring a single camera and a computing device. Preliminary simulations reveal that some proposed systems already provide a sufficient accuracy for 2D joint angle estimations.

Conclusions: Even though test results of different applications for some proposed techniques are promising, more rigour testing is required for validating their accuracy before they can be widely adopted in advanced rehabilitation applications.

Keywords: Computer vision; marker-less; motion analysis; telerehabilitation.

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Conflict of interest statement

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
A visualization of the repetitive stacked hourglass approach.
Figure 2.
Figure 2.
Components of a geometry-driven approach to human pose estimation.
Figure 3.
Figure 3.
A proposed multi-view 3D poses estimation system.
Figure 4.
Figure 4.
A framework of using single image to infer the shape and pose of a human body.
Figure 5.
Figure 5.
An illustration of a marker-less CV application measuring the knee joint angle.
Figure 6.
Figure 6.
After estimating the coordinates of hip, knee and ankle joints, the knee angle K estimation applies the law of cosines.
Figure 7.
Figure 7.
An illustration of a simulation tool calculating the level of accuracy for knee angle measurement for different error margins of joint coordinate estimations.
Figure 8.
Figure 8.
A marker-less system for analysing manual wheelchair propulsion.
Figure 9.
Figure 9.
A CV based marker-less motion capture method to assess 3D joint kinematics of symmetrical lifting tasks.
See this image and copyright information in PMC

References

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