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

Abstract

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 ( $R^2$ = 0.88-0.99) and for peak joint angles ( $IC{C}_{2,1}$ = 0.75-1.0) when compared with the gold standard marker-based motion capture system. Additionally, we observed high test-retest reliability ( $IC{C}_{3,1}$ = 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.