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

Abstract

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 ( $$R^{2}$$ R 2 = 0.88–0.99) and for peak joint angles ( $$ICC_{2,1}$$ I C 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 ( $$ICC_{3,1}$$ I C 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.