NDLI: Tracking motion context of railway passengers by fusion of low-power sensors in mobile devices

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Tracking motion context of railway passengers by fusion of low-power sensors in mobile devices

Content Provider	ACM Digital Library
Author	Higashino, Teruo Higuchi, Takamasa Yamaguchi, Hirozumi
Abstract	In this paper we develop StationSense, a novel mobile sensing solution for precisely tracking temporal stop-and-go patterns of railway passengers. While such motion context serves as a promising enabler of various traveler support systems, we found through experiments in a major railway network in Japan that existing accelerometer-based passenger tracking systems can poorly work in modern trains, where jolts during motion have been dramatically reduced. Towards robust motion tracking, StationSense harnesses characteristic features in ambient magnetic fields in trains to find candidates of stationary periods, and subsequently filters out false positive detections by a tailored acceleration fusion mechanism. Then it finds optimal boundaries between adjacent moving/stationary periods, employing unique signatures in accelerometer readings. Through field experiments around 16 railway lines, we show that StationSense can identify periods of train stops with accuracy of 81%, which is almost 2 times higher than the existing accelerometer-based solutions.
Starting Page	163
Ending Page	170
Page Count	8
File Format	PDF
ISBN	9781450335782
DOI	10.1145/2802083.2808387
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-09-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Mobile sensing Multi-sensor fusion Train stop detection
Content Type	Text
Resource Type	Article

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