An Approach to Analyzing and Recognizing Human Gait

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.;In this thesis, we present novel methods using image flow to identify different phases (double support, mid swing, toe off and heel strike) of a gait cycle. We use the torso excursion information and lower legs rotational velocities to identify these phases. The top ;Finally, we use histograms of normal flow to represent the motion patterns of different regions of the body. We measure the motion similarity between two image frames using the cosine similarity measure for comparing two histograms. Computing this measure between all the pairs of image frames in the two gait sequences gives a similarity matrix as a feature. These features are used in Support Vector Machines and Dynamic Programming together with the information about the phases of gait to compare two gait sequence. We demonstrate our approach on a publicly available gait dataset and present the analysis.;In summary, we establish that we can capture segmental data using a markerless gait analysis system. These data are sensitive, reliable and provide recognizable clinically relevant information about motion through all phases of gait.