Identification of Individual Walking Patterns Using Gait Acceleration

This paper presents an improved approach on identifying users based on three-dimensional gait acceleration signal characteristics produced by walking. When the user carries the wearable gait acceleration acquiring system, acceleration signals are registered by the accelerometer. Through dividing the signals into gait cycles, gait feature code which represents the walking pattern of the user can be extracted. Recognition is based on the general idea of template matching. We use dynamic time warping (DTW) algorithm for matching so that non-linear time normalization could be used to dispose the problems resulted from naturally occurring changes in walking speed. Experiments were performed on 35 healthy subjects walking on their normal speed; Equal Error Rate of 6.7% was achieved. Our preliminary experiments confirm the possibility of recognizing users based on their gait acceleration.