Two-dimensional video-based analysis of human gait using pose estimation

Author summary There is a growing interest among clinicians and researchers to use novel pose estimation algorithms that automatically track human movement to analyze human gait. Gait analysis is routinely conducted in designated laboratories with specialized equipment. On the other hand, pose estimation relies on digital videos that can be recorded from household devices such as a smartphone. As a result, these new techniques make it possible to move beyond the laboratory and perform gait analysis in other settings such as the home or clinic. Before such techniques are adopted, we identify a critical need for comparing outcome parameters against three-dimensional motion capture and to evaluate how camera viewpoint affect outcome parameters. We used simultaneous motion capture and left- and right-side video recordings of healthy human gait and calculated spatiotemporal gait parameters and lower-limb joint angles. We find that our provided workflow estimates spatiotemporal gait parameters together with hip and knee angles with the accuracy and precision needed to detect changes in the gait pattern. We demonstrate that the position of the participant relative to the camera affect spatial measures such as step length and discuss the limitations posed by the current approach.

GitHub - JRKagumba/2D-video-pose-estimation-yolov7: Computer-vision based monocular human pose-estimation and biomechanical gait analysis on elite runners. Using Yolov7 pose-estimation algorithm.

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