An algorithm for stance and swing phase detection of human gait cycle

The main objective of this work is to make an algorithm to detect onset and offset of stance and swing phase of human gait cycle. In order to proceed, the signal from the force sensors (placed underneath each foot) were first divided according to the fundamental period by auto-correlation. This ensures that each period contains a full stance phase and two fragmented swing phases. After this step, Short Time Fourier transform has been performed on each period of the signal to detect the initial search point (highest DC value) on the stance phase. Then by judging the width and slope of the straight line from changing search points on the signal to a changing destination point (starting from the minimum or maximum time value) of the graph the onset and offset of the stance and swing phase has been detected. The changing search point has small distance from the previous one, if the slope of the signal is small, but the distance increases with the increasing change of slope of the signal. The destination point changes with the change of slope of the straight line. This algorithm has been applied to the signals in the database of Parkinsons disease, which shows a success rate of more than 90%.