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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

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Mobile 3D motion capture frameworks can be integrated into a variety of mobile applications. Of particular interest are applications in the sports, health, and medical sector, where they enable use cases such as tracking of specific exercises in sports or rehabilitation, or initial health assessments before medical appointments.

Abstract

Computer-vision-based frameworks enable markerless human motion capture on consumer-grade devices in real-time. They open up new possibilities for application, such as in the health and medical sector. So far, research on mobile solutions has been focused on 2-dimensional motion capture frameworks. 2D motion analysis is limited by the viewing angle of the positioned camera. New frameworks enable 3-dimensional human motion capture and can be supported through additional smartphone sensors such as LiDAR. 3D motion capture promises to overcome the limitations of 2D frameworks by considering all three movement planes independent of the camera angle. In this study, we performed a laboratory experiment with ten subjects, comparing the joint angles in eight different body-weight exercises tracked by Apple ARKit, a mobile 3D motion capture framework, against a gold-standard system for motion capture: the Vicon system. The 3D motion capture framework exposed a weighted Mean Absolute Error of 18.80° ± 12.12° (ranging from 3.75° ± 0.99° to 47.06° ± 5.11° per tracked joint angle and exercise) and a Mean Spearman Rank Correlation Coefficient of 0.76 for the whole data set. The data set shows a high variance of those two metrics between the observed angles and performed exercises. The observed accuracy is influenced by the visibility of the joints and the observed motion. While the 3D motion capture framework is a promising technology that could enable several use cases in the entertainment, health, and medical area, its limitations should be considered for each potential application area.

Details

Title
Evaluating 3D Human Motion Capture on Mobile Devices
Author
Reimer, Lara Marie 1   VIAFID ORCID Logo  ; Kapsecker, Maximilian 1   VIAFID ORCID Logo  ; Fukushima, Takashi 2   VIAFID ORCID Logo  ; Jonas, Stephan M 3   VIAFID ORCID Logo 

 Department of Informatics, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany; [email protected]; Institute for Digital Medicine, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; [email protected] 
 Department of Sports and Health Sciences, Technical University of Munich, Georg-Brauchle-Ring 60/62, 80992 München, Germany; [email protected] 
 Institute for Digital Medicine, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; [email protected] 
First page
4806
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2670082036
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.