Evaluating 3D Human Motion Capture on Mobile Devices

被引:9
作者
Reimer, Lara Marie [1 ,2 ]
Kapsecker, Maximilian [1 ,2 ]
Fukushima, Takashi [3 ]
Jonas, Stephan M. [2 ]
机构
[1] Tech Univ Munich, Dept Informat, Boltzmannstr 3, D-85748 Garching, Germany
[2] Univ Hosp Bonn, Inst Digital Med, Venusberg Campus 1, D-53127 Bonn, Germany
[3] Tech Univ Munich, Dept Sports & Hlth Sci, Georg Brauchle Ring 60-62, D-80992 Munich, Germany
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 10期
关键词
human motion capture; mobile motion capture; optical motion capture; consumer electronics; mHealth; dHealth; CLINICAL GAIT ANALYSIS; RELIABILITY; VALIDITY; ACCURACY; SYSTEMS;
D O I
10.3390/app12104806
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Featured Application 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. 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 degrees +/- 12.12 degrees (ranging from 3.75 degrees +/- 0.99 degrees to 47.06 degrees +/- 5.11 degrees 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.
引用
收藏
页数:29
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