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Related Experiment Video

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A Rat Model of Ventricular Fibrillation and Resuscitation by Conventional Closed-chest Technique

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Indoor Positioning System Based on Chest-Mounted IMU.

Chuanhua Lu¹, Hideaki Uchiyama², Diego Thomas³

¹Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan. luchuanhua@limu.ait.kyushu-u.ac.jp.

Sensors (Basel, Switzerland)

|January 24, 2019

Summary

This summary is machine-generated.

This study introduces a novel chest-mounted inertial measurement unit (IMU) system for indoor pedestrian dead reckoning (PDR) navigation. It accurately estimates step length and altitude for multi-floor buildings, achieving high precision without external infrastructure.

Keywords:

accelerometers barometers gyroscopes inertial navigation map matching particle filters pedestrian dead reckoning

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Area of Science:

Robotics and Navigation
Sensor Fusion
Location-Based Services

Background:

Increasing demand for indoor navigation systems drives research into cost-effective solutions.
Inertial Measurement Unit (IMU)-based Pedestrian Dead Reckoning (PDR) offers a viable alternative to infrastructure-dependent systems.
Existing PDR systems often rely on zero-velocity updates, which are not applicable to all body-mounted IMU positions.

Purpose of the Study:

To propose a novel PDR system utilizing a chest-mounted IMU for enhanced indoor navigation.
To develop a new regression model for accurate step length estimation using only acceleration data from a chest-mounted IMU.
To integrate a particle filter-based map-matching algorithm and barometer data for improved 3D positioning and altitude estimation in multi-floor buildings.

Main Methods:

A novel regression model was developed to estimate step length directly from acceleration data acquired by a chest-mounted IMU.
A particle filtering approach was employed for efficient map matching to refine positioning and heading accuracy.
A barometer was integrated to provide altitude updates for multi-floor building navigation.

Main Results:

The proposed PDR system achieved a mean error of 5.2 meters over an 800-meter walking distance in the IPIN 2018 Competition.
The system demonstrated effective step length estimation and 3D positioning capabilities, including altitude tracking.
The novel approach successfully addressed the challenge of no zero-velocity updates for chest-mounted IMUs.

Conclusions:

The developed chest-mounted IMU-based PDR system offers a promising solution for accurate and cost-effective indoor navigation.
The novel regression model and map-matching integration enhance positioning accuracy, particularly in complex multi-floor environments.
The system's performance, validated in a competition setting, highlights its potential for real-world location-based services.