Model-driven statistical analysis of human gait motion

We describe a new method for analyzing and extracting human gait motion by combining statistical methods with image processing. The periodic motion of human gait is modeled by trigonometric-polynomial interpolant functions. The gait description is derived by topological analysis guided by medical studies that select areas from which joint angles are derived by regression analysis. Then, the interpolant functions are fitted to the gait data and whilst showing fidelity to earlier medical studies, also show recognition capability. As such, a new combination of medical knowledge, image processing and regression analysis can be used to label human motion in image sequences.