Robust and Accurate Step Counting Based on Motion Mode Recognition for Pedestrian Indoor Positioning Using a Smartphone

Abstract

Robust and accurate step counting plays an essential role in some fields, such as indoor positing, behavior recognition, and health management. Currently, there are numerous applications or methods in step counting. However, most solutions did not emphasize the elimination of false steps in the non-walking state, which still encounters the over-counting or under-counting problem. In this study, a robust and accurate step counting solution based on movement mode recognition is proposed. First, according to the characteristics of pedestrians walking, the SVM and FSM-DT classifiers were constructed to recognize the user’s motion state and phone usage mode, respectively. The purpose of classification is to solve the step-counting error of non-walking state and initialize a suitable parameter for different cases. Then, we utilized a crest-valley detection algorithm with multi-feature constrained to detect the step, and the initial threshold values for each mode can be adjusted adaptively during walking. The results indicate that the accuracy of the proposed method can reach 99.11% and 97.43% in normal and free walking. Compared with standard peak detection and an excellent method, the proposed method can improve by 44.14% and 18.71% for false walking, respectively, which is a significant improvement in the robustness and accuracy. Furthermore, we also achieve an average accuracy higher than 98% in some typically carrying modes and ways, and higher accuracy compared with four popular step-counting mobile applications in different walking states.