Using Image Flow to Analyze Human Gait

Marker-based imaging of human locomotion provides an extremely high level of accuracy, but it is quite intrusive and requires a significant amount of time for both the subject and the gait analyst. The purpose of automated gait analysis is to provide a means to analyze gait from video without the use of markers. Performing this analysis in an automated manner opens up a number of possibilities such as continuous analysis to monitor a course of treatment or to keep watch on the elderly population for changes in gait that might indicate a physical injury or change in mental condition.;There are a number of factors that play into automated gait analysis. Different aspects (or determinants) of gait are active at different parts of the gait cycle. Therefore to provide analysis with respect to all determinants we must have a way of including gait cycle information. There is also the question of how the motion of the limbs can be analyzed. Limbs are constantly self-occluding, and issues such as poor contrast and loose clothing clutter the true motion of the limbs. Actual motion represents the ground truth of how the limb is moving, often times assumed in clinical analysis whereas in automated analysis this is not a given. Loose clothing may obscure actual limb motion, and motion analysis can only provide information about the apparent motion. For these reasons, we see automated gait analysis in some respects as complementary to marker based imaging of gait. It is not possible to have the same level of precision, but the availability and ease of this approach makes it much more applicable to a wider range of scenarios.;In this thesis, we present an approach to automated gait analysis based on the motion of superpixels. We overlay the silhouette of the subject with a regular grid, where each grid cell represents a single superpixel. We overlay an additional superpixel to the top 13% of the body, which approximately corresponds to the head region. Human motion analysis is accomplished by analyzing the motion in each of the superpixels. We model the motion of the head using an affine motion model, which can account for a wide variety of valid motions that we can observe in the head (bending at the neck turning in a different direction, etc.). We use a three parameter "twist" motion model on the other regions of the body, which only models the translation and rotation in the superpixel.;Finally, we build a representation of the data using independent components analysis (ICA). ICA provides a compact set of features describing the shape and motion of the body. We use independent components of motion to answer two different questions. The first: can we identify characteristics of the subject (i.e. gender and heel height) given shape and motion information. This is mostly important for identification in a soft biometrics sense. The second: can we identify a person that is walking in a similar manner using ICs. We demonstrate the robustness of the approach by taking it one step further by using ICs of each individual patch to compare the gaits of two individuals, and to give reasons why their gaits are similar and different.