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Proceedings ArticleDOI

Estimate a user's location using smartphone's barometer on a subway

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TLDR
A localization method that uses only a barometer on a smartphone to estimate motion state from the change of elevation, and also estimate latest stop station by the similarity of a series of elevations recorded when the train stopped and actual elevations of stations.
Abstract
Knowing the location of a train is necessary to develop a useful service for train passengers. However, popular localization methods such as GPS and Wi-Fi are not accurate especially on a subway. As an alternative method, estimation of motion state and stop station by using sensors on a smartphone is being studied for subway passengers. This paper proposes a localization method that uses only a barometer on a smartphone. We estimate motion state from the change of elevation, and also estimate latest stop station by the similarity of a series of elevations recorded when the train stopped and actual elevations of stations. By estimation of the motion state and the latest stop station, development of various context-aware services for subway passengers becomes possible. Through experiments in four lines of subway in Tokyo, we demonstrated that the accuracy of estimation of the motion state is 86%, and estimation of the stop station is 58%.

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Citations
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Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

TL;DR: This work attempts to review the process of inferring meaningful data from smart devices’ sensors, especially, smartphones, and different useful machine learning applications based on smartphones’ sensor data are shown.
Proceedings ArticleDOI

CanalSense: Face-Related Movement Recognition System based on Sensing Air Pressure in Ear Canals

TL;DR: It is found that face-related movements change air pressure inside the ear canals, which shows characteristic changes depending on the type and degree of the movement, and such characteristic changes can be used to recognize face- related movements.
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On the Challenges and Potential of Using Barometric Sensors to Track Human Activity

TL;DR: This review article thoroughly details all the factors affecting the atmospheric pressure, and presents a comprehensive report of the numerous studies dealing with one or more of these factors in the particular framework of human activity tracking and recognition.
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Crowdsensing-Based Cross-Operator Switch in Rail Transit Systems

TL;DR: A robust Crowdsensing based Switch (CrowdSwitch) solution dedicated for rail transit systems that utilizes mass mobile devices of passengers to sense and collect LTE signals along subway tracks, builds the signal heat map (S-Map) in the cloud servers, and recommends the optimal switch taking advantages of the known tracks.
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Wireless muometric navigation system

TL;DR: In this article , a more versatile, wireless muometric navigation system (MuWNS), which was designed in conjunction with a cost-effective, crystal-oscillator-based grandmaster clock and a performance evaluation is reported for shallow underground/indoor, deep underground and undersea environments.
References
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Proceedings ArticleDOI

Using mobile phone barometer for low-power transportation context detection

TL;DR: This is the first paper that uses only the barometer for context detection, which uses 32 mW lower power compared to Google, and has comparable accuracy to both Google and FMS.
Journal ArticleDOI

Underground Aesthetics: Rethinking Urban Computing

TL;DR: Reflection on the ethnographic study results and the undersound design suggests how an aesthetic account of urban life might be the basis of designs that support not only an individual but also a collective experience of the city.
Proceedings ArticleDOI

SubwayPS: towards smartphone positioning in underground public transportation systems

TL;DR: In this paper, the authors introduce the problem of underground transport positioning on smartphones and present SubwayPS, an accelerometer-based positioning technique that allows smartphones to determine their location substantially better than baseline approaches, even deep beneath city streets.
Proceedings ArticleDOI

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

TL;DR: It is shown that StationSense can identify periods of train stops with accuracy of 81%, which is almost 2 times higher than the existing accelerometer-based solutions.
Proceedings ArticleDOI

At Which Station Am I?: Identifying Subway Stations Using Only a Pressure Sensor

TL;DR: This paper presents the first study to identify a current station in a large subway network using only a pressure sensor, and achieved 85 % accuracy to infer at which station the authors are.
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