Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data

Ceron, Jesus D.; Kluge, Felix; Küderle, Arne; Eskofier, Bjoern M.; López, Diego M.

doi:10.3390/s20174742

Cited by 10 publications

(4 citation statements)

References 26 publications

(47 reference statements)

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“…They can be used to analyze users' locations and control appliances in the surrounding area [11,12]. Alternatively, such systems can use Beacon technology with an inertial measurement unit (IMU) to record the user's historical movement trajectory [13].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an Indoor Warning System with Physiological Signal Monitoring for People Isolated at Home

Hung

Fanjiang

Lee

et al. 2022

Sensors

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Due to the recent COVID-19 pandemic, many people have faced in-home isolation, as every suspected patient must stay at home. The behavior of such isolated people needs to be monitored to ensure that they are staying at home. Using a camera is a very practical method. However, smart bracelets are more convenient when personal privacy is a concern or when the blood oxygen value or heart rate must be monitored. In this study, a low-cost indoor positioning system that uses a Bluetooth beacon, a smart bracelet, and an embedded system is proposed. In addition to monitoring whether a person living alone is active in a specific environment and tracking the heart rate or blood oxygen value under particular conditions, this system can also send early warning signals to specific observation units or relatives through instant messaging software.

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Section: Introductionmentioning

confidence: 99%

Design and Implementation of an Indoor Warning System with Physiological Signal Monitoring for People Isolated at Home

Hung

Fanjiang

Lee

et al. 2022

Sensors

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show abstract

“…Therefore, inertial navigation systems should be integrated with other sensors or aiding information to improve the navigation accuracy [15]. References [16][17][18][19][20][21] use Bluetooth, UWB, Wi-Fi sensor information to correct inertial navigation drift errors, respectively. Multi-sensor fusion positioning methods are all effective in providing users with efficient indoor positioning navigation.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Inertial Navigation with Building Floor Plans for Indoor Environments via Particle Filter

Zhang¹,

Zhang²,

Guo³

et al. 2022

Journal of Compurters

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<p>With the rapid development of smart city, the indoor positioning services became more and more important. During the existing solutions, inertial measurement unit (IMU) with pedestrian dead reckoning (PDR) was a promising scheme since they did not require external equipment in the environment. However, the orientation drift of low-cost IMU limited their application in practical. To address this problem, a zero-velocity update (ZUPT) framework included Kalman filter and particle filter is designed based on the foot-based low-cost IMU and digital floor plan to provide the service of personal navigation. In the designed Smoothing for ZUPT-aided INSs framework, the Kalman filter is used to estimate the position and attitude by zero velocity correction technique. Then, the particle filter is used to improve the localization and heading accuracy by map matching. The position estimation presented in this study achieves an average position error of 1.16 m. The experimental results show that the designed framework can solve the personal navigation problem in the case of building plan information assistance and help improve the accuracy and reliability of continuous position determination of personal navigation systems effectively. </p> <p> </p>

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“…In an indoor environment, the GPS performance is degraded owing to environmental changes and obstacles that contribute to signal fluctuation and noise. Indoor localization is essential for a diverse range of applications such as pedestrian navigation [1], [2], disaster management [3], health monitoring [4], smart parking [5], smart cities [6], and smart buildings [7]. Moreover, because most individuals typically spend 80% of their time indoors conducting different tasks, in recent years, an accurate indoor positioning system (IPS) has become a research priority [8].…”

Section: Introductionmentioning

confidence: 99%

Floor Map-Aware Particle Filtering Based Indoor Navigation System

Junoh

Subedi²,

Pyun

2021

IEEE Access

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Smartphone-based indoor navigation systems are becoming increasingly popular in a variety of applications. However, localization accuracy has always been a challenge. The Kalman filter (KF) is a well-known estimation in the Bayesian framework, but can only deal with linear problems and Gaussian models. A particle filter (PF) is another essential estimation tool in a Bayesian system. However, a critical challenge with PF is the problem of particle degradation after resampling. To mitigate the particle degradation problem in PF, unsupervised learning based on k-means clustering is proposed in this paper. It forms clusters of similar particles based on the sum of weights. Also, we present enhancing the PF by utilizing a map constraint and k-means clustering (PFMK) and integrating Bluetooth low energy (BLE) along with pedestrian dead reckoning (PDR) for positioning. BLE and PDR-based positioning with a map constraint lead to an increase in accuracy of at least 20% compared with a traditional PF. Moreover, the proposed unsupervised k-means approach increases the accuracy by an additional 20%, whereas the overall performance of PFMK achieves a mean error of <1.5 m in the test environments.INDEX TERMS Bluetooth low energy (BLE) beacons, indoor navigation, map constraints, particle filter, unsupervised learning.

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Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data

Cited by 10 publications

References 26 publications

Design and Implementation of an Indoor Warning System with Physiological Signal Monitoring for People Isolated at Home

Design and Implementation of an Indoor Warning System with Physiological Signal Monitoring for People Isolated at Home

Pedestrian Inertial Navigation with Building Floor Plans for Indoor Environments via Particle Filter

Floor Map-Aware Particle Filtering Based Indoor Navigation System

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