Development of an Adaptive Gait Characterizer
The study of human gait remains one of the finest areas of biomechanics. Commonly available gait characterization systems provide information regarding speed, cadence and calories burnt during an exercise session. However, the gait of an individual changes continuously with every step. Thus, this work is inspired by the need to develop a system which enables real time analysis of critical spatio-temporal gait parameters while adapting to the walking pattern of the subject. The study of these parameters for a given subject enables us to perform an analysis of individualized gait pattern thereby allowing us to develop corrective measures for gait deficits. In this work we present a real-time analysis of intra-limb temporal parameters using the proposed system that adapts to the changing walking speed. 1
We present a connectionist model designed for supervised learning of associated patterns, which is based on Kohonen’s self-organizing feature maps. While learning, the classification is performed on both inputs and desired associated ouputs. The learned weights are then used in exploitation phase to associate an input vector with an output one.