Abstract:
Gait analysis has been an active area of research in computer vision for a long time. It is also important for rehabilitation science where clinicians explore innovative ways helping to analyze gait of different people. The traditional ways to study gait rely on 3D optical motion capture systems which involve the use of cumbersome active/passive markers to be placed on a subject's body. The attachment of markers to the segments hinder natural patterns of movement and may lead to altered gait information. Automated gait analysis has been proposed as a solution to this problem. The aim of automated gait analysis is to provide information about the gait parameters and gait determinants from video without using markers. Gait is a repetitive, highly constrained and periodic activity. Different gait determinants are active in different phases of the gait cycle to minimize the excursion of the body's center of gravity and help produce forward progression with the least expenditure of energy. The motion of limb segments encode information about different phases of gait cycle. However, estimating the motion of limbs from the videos is challenging since limbs are self occluding and only apparent motion can be observed using the images. To add to the issue, the quality of the recorded video (color contrast, cluttered background) and clothing worn by the subject can play a significant role in the computation of that apparent motion.
