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

SUBMITTER: Bae K 

PROVIDER: S-EPMC11603033 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature