Automatic gait characterization for a mobility assistance system

This paper addresses gait analysis for a mobility assistance robot designed for the elderly people. Six patients and ten healthy peoples were invited to be part of our first pilot experiment. We designed two experiments so as to firstly detect gait parameters and secondly to identify a change of speed. For the first trial, we compared the temporal-distance parameters of the healthy people and of individuals suffering of mobility problems. The percentages of the gait cycle for duration of stance are higher for people with mobility impairment than for healthy people. In the second experiment, we detected the change in walking speed from the ten healthy peoples. Two different metrics derived from the Kullback-Leibler (KL) divergence and from the Generalized Likelihood Ratio (GLR) were employed for walking change detection. The Receiver Operating Characteristic (ROC) curves show a better performance for the signal obtained with the accelerometer sensor than that obtained with the infrared distance sensor. Nevertheless, the results of our experiments demonstrated that both methodologies (KL and GLR) can be used to detect the change points during walking at high or slow speed.