Novel method for stride length estimation with body area network accelerometers

Abstract

Gait analysis using wireless accelerometers deployed as body area networks can provide valuable information for multiple health-related applications. Within this field, stride length estimation represents a difficult task. In this paper we present a novel method to estimate stride length through the application of the wavelet transform to the signal obtained from a wireless accelerometer on the waist. We also introduce a novel metric to determine the level of the wavelet transform detail coefficients from which the step frequency can be directly extracted. Additionally, we show the correlation between the energy of the wavelet transform approximation coefficients and the speed of the gait.