Concurrent validity and test reliability of the deep learning markerless motion capture system during the overhead squat

被引:1
|
作者
Bae, Kyungun [1 ,3 ]
Lee, Seyun [2 ]
Bak, Se-Young [1 ]
Kim, Hyo Sang [1 ]
Ha, Yuncheol [1 ]
You, Joshua H. [3 ]
机构
[1] Naver, Hlth Care Lab, Seongnam 13561, South Korea
[2] NaverCloud, Seongnam 13561, South Korea
[3] Yonsei Univ, Sports Movement Artificial Intelligence Robot Tech, Dept Phys Therapy, Wonju 26493, South Korea
来源
SCIENTIFIC REPORTS | 2024年 / 14卷 / 01期
基金
新加坡国家研究基金会;
关键词
HUMAN JOINT MOTION; ISB RECOMMENDATION; DEFINITIONS; STROKE; INJURY;
D O I
10.1038/s41598-024-79707-2
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Marker-based optical motion capture systems have been used as a cardinal vehicle to probe and understand the underpinning mechanism of human posture and movement, but it is time-consuming for complex and delicate data acquisition and analysis, labor-intensive with highly trained operators. To mitigate such inherent issues, we developed an accurate and usable (5-min data collection and processing) deep-learning-based 3-Dimensional markerless motion capture system called "Ergo", designed for use in ecological digital healthcare environments. We investigated the concurrent validity and the test-retest reliability of the Ergo system measurement's whole body joint kinematics (time series joint angles and peak joint angles) data by comparing it with a standard marker-based motion capture system recorded during an overhead squat movement. The Ergo system demonstrated excellent agreement for time series joint angles (R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R<^>{2}$$\end{document} = 0.88-0.99) and for peak joint angles (ICC2,1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ICC_{2,1}$$\end{document} = 0.75-1.0) when compared with the gold standard marker-based motion capture system. Additionally, we observed high test-retest reliability (ICC3,1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ICC_{3,1}$$\end{document} = 0.92-0.99). In conclusion, the deep learning-based markerless Ergo motion capture system considerably shows comparable performance with the Gold Standard marker-based motion capture system measurements in the concurrent accuracy, reliability, thereby making it a highly accessible choice for diverse universal users and ecological industries or environments.
引用
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页数:11
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