  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.
