Markerless identification of key events in gait cycle using image flow

Abstract

Gait analysis has been an interesting area of research for several decades. In this paper, we propose image-flow-based methods to compute the motion and velocities of different body segments automatically, using a single inexpensive video camera. We then identify and extract different events of the gait cycle (double-support, mid-swing, toe-off and heel-strike) from video images. Experiments were conducted in which four walking subjects were captured from the sagittal plane. Automatic segmentation was performed to isolate the moving body from the background. The head excursion and the shank motion were then computed to identify the key frames corresponding to different events in the gait cycle. Our approach does not require calibrated cameras or special markers to capture movement. We have also compared our method with the Optotrak 3D motion capture system and found our results in good agreement with the Optotrak results. The development of our method has potential use in the markerless and unencumbered video capture of human locomotion. Monitoring gait in homes and communities provides a useful application for the aged and the disabled. Our method could potentially be used as an assessment tool to determine gait symmetry or to establish the normal gait pattern of an individual.