Detection of Pedestrian Turning Motions to Enhance Indoor Map Matching Performance

TL;DR

This paper develops and compares advanced algorithms for detecting pedestrian turning motions using smartphone IMU data to improve indoor navigation accuracy, especially for search and rescue operations.

Contribution

It introduces enhanced turn detection algorithms, including HMM and PELT methods, that outperform existing threshold-based approaches in indoor pedestrian navigation.

Findings

HMM-based method achieved 5.14% missed detection rate

PELT-based method reduced missed detection to 8.93%

HMM method had the lowest false alarm rate of 2.00%

Abstract

A pedestrian navigation system (PNS) in indoor environments, where global navigation satellite system (GNSS) signal access is difficult, is necessary, particularly for search and rescue (SAR) operations in large buildings. This paper focuses on studying pedestrian walking behaviors to enhance the performance of indoor pedestrian dead reckoning (PDR) and map matching techniques. Specifically, our research aims to detect pedestrian turning motions using smartphone inertial measurement unit (IMU) information in a given PDR trajectory. To improve existing methods, including the threshold-based turn detection method, hidden Markov model (HMM)-based turn detection method, and pruned exact linear time (PELT) algorithm-based turn detection method, we propose enhanced algorithms that better detect pedestrian turning motions. During field tests, using the threshold-based method, we observed a missed detection rate of 20.35% and a false alarm rate of 7.65%. The PELT-based method achieved a significant improvement with a missed detection rate of 8.93% and a false alarm rate of 6.97%. However, the best results were obtained using the HMM-based method, which demonstrated a missed detection rate of 5.14% and a false alarm rate of 2.00%. In summary, our research contributes to the development of a more accurate and reliable pedestrian navigation system by leveraging smartphone IMU data and advanced algorithms for turn detection in indoor environments.