English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Detection of Pedestrian Turning Motions to Enhance Indoor Map Matching Performance