Statistical modeling for human gait analysis based on field data

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In this study we studied the problem of analyzing and classifying frontal view human gait by registration and modeling on a video data. Firstly we created the statistical walking model with constant speed and succeeded in estimating parameters included in the gait models with the scale changing parameter and the speed parameter that indicates the speed of human walking. We also investigated the model and estimation accuracy with the motion capture system which gave us precise measurements of trajectories of moving objects. Our basic statistical gait model only includes two parameters of registration and speed and we can easily estimate the parameters from frontal view gait video data. Secondly, we extended this simple model to handle the speed changing and other human movement parameters. Collins et al. (2009) has reported that arm swing is an very important role in the gait motion based on the simple gait model. We considered the human gait modeling based Collins et al. (2009).