A method of personal positioning for indoor customer tracking utilizing wearable inertial sensors

Yasuaki Ohtaki; Dan Hu; Koichi Hashimoto; Hikaru Inooka

doi:10.1117/12.664238

A method of personal positioning for indoor customer tracking utilizing wearable inertial sensors

Yasuaki Ohtaki, Dan Hu, Koichi Hashimoto, Hikaru Inooka

INF - System Information Sciences

Research output: Contribution to journal › Conference article › peer-review

Abstract

In order to achieve in-store customer traffic tracking, a method should be capable of catching personnel routings and ambulation trajectories while shopping. In this study, we presented a practical method for indoor personnel positioning, especially focusing on ambulatory path recognition and trajectory estimation. The method was designed to be autonomous needless of neither external measures nor cumbersome installations to a store environment. To avoid an inherent problem of trajectory estimation through Dead-Reckoning algorithm, an advanced probabilistic Map Matching method was applied utilizing a Particle filtering technique. The experiment was performed to confirm the utility of the proposed method in a small retail store. The result showed that the proposed method provided feasible successes in tracking personnel positioning.

Original language	English
Article number	60401W
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	6040
DOIs	https://doi.org/10.1117/12.664238
Publication status	Published - 2005 Dec 1
Event	ICMIT 2005: Mechatronics, MEMS and Smart Materials - Changchun, China Duration: 2005 Sep 20 → 2005 Sep 23

Keywords

Dead Reckoning
Human Walking
Inertial Sensor
LocatiOn Estimation
Map Matching
Medical and Health Welfare Instruments
Navigation
Particle Filter
Personnel Positioning

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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title = "A method of personal positioning for indoor customer tracking utilizing wearable inertial sensors",

abstract = "In order to achieve in-store customer traffic tracking, a method should be capable of catching personnel routings and ambulation trajectories while shopping. In this study, we presented a practical method for indoor personnel positioning, especially focusing on ambulatory path recognition and trajectory estimation. The method was designed to be autonomous needless of neither external measures nor cumbersome installations to a store environment. To avoid an inherent problem of trajectory estimation through Dead-Reckoning algorithm, an advanced probabilistic Map Matching method was applied utilizing a Particle filtering technique. The experiment was performed to confirm the utility of the proposed method in a small retail store. The result showed that the proposed method provided feasible successes in tracking personnel positioning.",

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author = "Yasuaki Ohtaki and Dan Hu and Koichi Hashimoto and Hikaru Inooka",

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journal = "Proceedings of SPIE - The International Society for Optical Engineering",

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AU - Ohtaki, Yasuaki

AU - Hu, Dan

AU - Hashimoto, Koichi

AU - Inooka, Hikaru

PY - 2005/12/1

Y1 - 2005/12/1

N2 - In order to achieve in-store customer traffic tracking, a method should be capable of catching personnel routings and ambulation trajectories while shopping. In this study, we presented a practical method for indoor personnel positioning, especially focusing on ambulatory path recognition and trajectory estimation. The method was designed to be autonomous needless of neither external measures nor cumbersome installations to a store environment. To avoid an inherent problem of trajectory estimation through Dead-Reckoning algorithm, an advanced probabilistic Map Matching method was applied utilizing a Particle filtering technique. The experiment was performed to confirm the utility of the proposed method in a small retail store. The result showed that the proposed method provided feasible successes in tracking personnel positioning.

AB - In order to achieve in-store customer traffic tracking, a method should be capable of catching personnel routings and ambulation trajectories while shopping. In this study, we presented a practical method for indoor personnel positioning, especially focusing on ambulatory path recognition and trajectory estimation. The method was designed to be autonomous needless of neither external measures nor cumbersome installations to a store environment. To avoid an inherent problem of trajectory estimation through Dead-Reckoning algorithm, an advanced probabilistic Map Matching method was applied utilizing a Particle filtering technique. The experiment was performed to confirm the utility of the proposed method in a small retail store. The result showed that the proposed method provided feasible successes in tracking personnel positioning.

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KW - Human Walking

KW - Inertial Sensor

KW - LocatiOn Estimation

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KW - Medical and Health Welfare Instruments

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KW - Personnel Positioning

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