Abstract
In recent years, several systems have been developed to capture human motion in real-time using common RGB cameras. This approach has great potential to become widespread among the general public as it allows the remote evaluation of exercise at no additional cost. The concept of using these systems in rehabilitation in the home environment has been discussed, but no work has addressed the practical problem of detecting basic body parts under different sensing conditions on a large scale. In this study, we evaluate the ability of the OpenPose pose estimation algorithm to perform keypoint detection of anatomical landmarks under different conditions. We infer the quality of detection based on the keypoint confidence values reported by the OpenPose. We used more than two thousand unique exercises for the evaluation. We focus on the influence of the camera view and the influence of the position of the trainees, which are essential in terms of the use for home exercise. Our results show that the position of the trainee has the greatest effect, in the following increasing order of suitability across all camera views: lying position, position on the knees, sitting position, and standing position. On the other hand, the effect of the camera view was only marginal, showing that the side view is having slightly worse results. The results might also indicate that the quality of detection of lower body joints is lower across all conditions than the quality of detection of upper body joints. In this practical overview, we present the possibilities and limitations of current camera-based systems in telerehabilitation.
Highlights
The general concept of remote rehabilitation using motion capture (MoCap) systems has undergone a turbulent change in recent years, as there are several tools for threedimensional assessments, including sophisticated automation technologies and algorithms, often costing time, expensive equipment and inapplicable inconvenience to the daily practice [1]
A large number of MoCap systems detecting the pose of a human using a “markerless” approach have emerged [3], these systems work without the necessity of placement of any markers on the human body [4]
We present the results of our findings using OpenPose reported confidence values
Summary
The general concept of remote rehabilitation using motion capture (MoCap) systems has undergone a turbulent change in recent years, as there are several tools for threedimensional assessments, including sophisticated automation technologies and algorithms, often costing time, expensive equipment and inapplicable inconvenience to the daily practice [1]. A large number of MoCap systems detecting the pose of a human using a “markerless” approach have emerged [3], these systems work without the necessity of placement of any markers on the human body [4] This approach reduces the technical and financial requirements and complexity of arrangement [5] and it can be found in the context of distance medicine, in specialized clinics and in the home environment [6]. Considering the application of distance medicine in the home environment, the most promising systems seem to be systems for the evaluation of body movements from commonly used standard video (RGB) records [7] In this case, we only need a regular camera, which is currently integrated into most common electronic devices, such as smartphones or laptops, or smart TVs
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
