Step Length Estimation and Activity Detection in a PDR System Based on a Fuzzy Model with Inertial Sensors

Abstract

This chapter presents an approach on pedestrian dead reckoning (PDR) which incorporates activity classification over a fuzzy inference system (FIS) for step length estimation. In the proposed algorithm, the pedestrian is equipped with an inertial measurement unit attached to the waist, which provides three-axis accelerometer and gyroscope signals. The main goal is to integrate the activity classification and step-length estimation algorithms into a PDR system. In order to improve the step-length estimation, several types of activities are classified using a multi-layer perceptron (MLP) neural network with feature extraction based on statistical parameters from wavelet decomposition. This work focuses on classifying activities that a pedestrian performs routinely in his daily life, such as walking, walking fast, jogging and running. The step-length is dynamically estimated using a multiple-input–single-output (MISO) fuzzy inference system. Results provide an average classification rate of 87.49 % with an accuracy on step-length estimation about 92.57 % in average.