Reliability and validity of current computer vision based motion capture systems in gait analysis: A systematic review

Abstract

Background

Traditional instrumented gait analysis (IGA) objectively quantifies gait deviations, but its clinical use is hindered by high cost, lab environment, and complex protocols. Pose estimation algorithm (PEA)-based gait analysis, which infers joint positions from videos, offers an accessible method to detect gait abnormalities and tailor rehabilitation strategies. However, its reliability and validity in gait analysis and algorithmic factors affecting accuracy have not been reviewed.

Research question

This systematic review aims to evaluate the accuracy of PEA-based gait analysis systems and to identify the algorithmic factors impacting their accuracy.

Method

A total of 644 articles were initially identified through Scopus, PubMed, and IEEE, with 20 meeting the inclusion and exclusion criteria. Reliability, validity, and algorithmic parameters were extracted for detailed review.

Results and significance

Most included articles focus on validity against the gold standard, while limited evidence makes it challenging to determine reliability. OpenCap demonstrated an MAE of 4.1° for 3D joint angles, but higher errors in rotational angles require further validation. OpenPose demonstrated ICCs of 0.89–0.994 for spatiotemporal parameters and MAE < 5.2° for 2D hip and knee joint angles in the sagittal plane (ICCs = 0.67–0.92, CCCs = 0.83–0.979), but ankle kinematics exhibited poor accuracy (ICCs = 0.37–0.57, MAEs = 3.1°-9.77°, CCCs = 0.51–0.936). PEA accuracy depends on camera settings, backbone architecture, and training datasets. This study reviews the accuracy of PEA-based gait analysis systems, supporting future research in gait-related clinical applications of PEA.