An acceleration based heuristics algorithm for gait phase detection

Abstract

Detection of gait phase has multiple of applications such as lower limb prosthetics, drop foot stimulator, and rehabilitation assessment. Different types of wearable sensors such as force sensitive sensors (FSR), gyroscopes, and the combination of gyroscopes and accelerometers, have been used for the classification of gait phase. Among these sensors, since accelerometers are low in cost, easy to use, reliable, and lower power consumption, they have been widely used in some wearable devices. The accelerometer-based methods for gait phase classification have been also proposed in few previous studies. In this paper, we developed an acceleration-signal-based heuristics algorithm to detect the gait phase, which could divide the gait phase into loading response (LR), mid-stance (MS), terminal stance (TS), and swing phase (SW). In the proposed algorithm, the corresponding gait events of the four phases were detected using local inflection points and curve turning points from the filtered composite acceleration signal. The performance of the proposed algorithm was evaluated with ten healthy subjects walking on a level ground at their comfortable speed for 60 s. The preliminary results showed that the proposed algorithm is reliable and accurate in classifying the four gait phases (LR, MS, TS, and SW).