Pedestrian Dead Reckoning for Multiple Walking Styles Using Classifier-Based Step Detection

Abstract

Traditional pedestrian dead reckoning (PDR) systems have been designed for scenarios where users walk straight ahead. However, user behavior observation at the museum revealed that users often stop or walk sideways to look at the exhibits. If the user's smartphone is moving when the user is stopped, false step detection may occur. In addition, the correct step or change of direction may not be detected in sideways walking. To solve these problems, we propose a novel PDR system. First, we classify the user's walking style to address the problems of false step detection and undetected changes of direction. Next, we use a classifier to detect when the foot touches the ground from smartphone sensor data and perform step detection. Compared with the existing SmartPDR, our proposed method improved positioning accuracy by 20% in straight walking and 70% in sideways walking.