Development of a low-cost reliable indoor localization system targeted to older users with mild cognitive impairments

Recently, indoor localization has witnessed an increase in interest, due to the potential wide range of using in different applications, such as Internet of Things (IoT). It is also providing a solution for the absence of Global Positioning System (GPS) signals inside buildings. Different techniques have been used for performing the indoor localization, such as sensors and wireless technologies. In this paper, an indoor localization and object tracking system is proposed based on WiFi transmission technique. It is done by distributing different WiFi sources around the building to read the data of the tracked objects. This is to measure the distance between the WiFi receiver and the object to allocate and track it efficiently. The test results show that the proposed system is working in an efficient way with low cost.

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A low cost indoor localization system for mobile robot experimental setup

Journal of Physics Conference Series ◽

10.1088/1742-6596/1007/1/012055 ◽

2018 ◽

Vol 1007 ◽

pp. 012055

Author(s):

S Adinandra ◽

A Syarif

Keyword(s):

Mobile Robot ◽

Indoor Localization ◽

Low Cost ◽

Experimental Setup ◽

Localization System

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A Low-Cost Indoor Activity Monitoring System for Detecting Frailty in Older Adults

Sensors ◽

10.3390/s19030452 ◽

2019 ◽

Vol 19 (3) ◽

pp. 452 ◽

Cited By ~ 10

Author(s):

Thomas Tegou ◽

Ilias Kalamaras ◽

Markos Tsipouras ◽

Nikolaos Giannakeas ◽

Kostantinos Votis ◽

...

Keyword(s):

Older People ◽

Indoor Localization ◽

Low Cost ◽

Clinical Status ◽

Localization System ◽

Frailty Status ◽

Asset Tracking ◽

Commercial Applications ◽

Tracking Device ◽

Localization Systems

Indoor localization systems have already wide applications mainly for providing localized information and directions. The majority of them focus on commercial applications providing information such us advertisements, guidance and asset tracking. Medical oriented localization systems are uncommon. Given the fact that an individual’s indoor movements can be indicative of his/her clinical status, in this paper we present a low-cost indoor localization system with room-level accuracy used to assess the frailty of older people. We focused on designing a system with easy installation and low cost to be used by non technical staff. The system was installed in older people houses in order to collect data about their indoor localization habits. The collected data were examined in combination with their frailty status, showing a correlation between them. The indoor localization system is based on the processing of Received Signal Strength Indicator (RSSI) measurements by a tracking device, from Bluetooth Beacons, using a fingerprint-based procedure. The system has been tested in realistic settings achieving accuracy above 93% in room estimation. The proposed system was used in 271 houses collecting data for 1–7-day sessions. The evaluation of the collected data using ten-fold cross-validation showed an accuracy of 83% in the classification of a monitored person regarding his/her frailty status (Frail, Pre-frail, Non-frail).

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A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction

Sensors ◽

10.3390/s21196447 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6447

Author(s):

Keliu Long ◽

Darryl Franck Nsalo Kong ◽

Kun Zhang ◽

Chuan Tian ◽

Chong Shen

Keyword(s):

Indoor Localization ◽

Low Cost ◽

Indoor Positioning ◽

Ultra Wideband ◽

Localization System ◽

Initial Stage ◽

Positioning Stage ◽

Two Stages ◽

Indoor Positioning System

A fingerprint-based localization system is an economic way to solve an indoor positioning problem. However, the traditional off-line fingerprint collection stage is a time-consuming and laborious process which limits the use of fingerprint-based localization systems. In this paper, based on ubiquitous Wireless Fidelity (Wi-Fi) equipment and a low-cost Ultra-Wideband (UWB) ranging system (with only one UWB anchor), a ready-to-use indoor localization system is proposed to realize long-term and high-accuracy indoor positioning. More specifically, in this system, it is divided into two stages: (1) an initial stage, and (2) a positioning stage. In the initial stage, an Inertial Measure Unit (IMU) is used to calculate the position using Pedestrian Dead Reckon (PDR) algorithm within a preset number of steps, and the location-related fingerprints are collected to train a Convolutional Neural Network (CNN) regression model; simultaneously, in order to make the UWB ranging system adapt to the Non-Line-of-Sight (NLoS) environment, the increments of acceleration and angular velocity in IMU and the increments of single UWB ranging measures are correlated to pre-train a Supported Vector Regression (SVR). After reaching the threshold of time or step number, the system is changed into a positioning stage, and the CNN predicts the position calibrated by corrected UWB ranging. At last, a series of practical experiments are conducted in the real environment; the experiment results show that, due to the corrected UWB ranging measures calibrating the CNN parameters in every positioning period, this system has stable localization results in a comparative long-term range. Additionally, it has the advantages of stability, low cost, anti-noise, etc.

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An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM

Sensors ◽

10.3390/s21082857 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2857

Author(s):

Simon Tomažič ◽

Igor Škrjanc

Keyword(s):

Data Acquisition ◽

Indoor Localization ◽

Low Cost ◽

Path Loss ◽

Signal Strength ◽

Localization Algorithm ◽

Localization Accuracy ◽

Localization System ◽

Good Trade ◽

Strength Modeling

Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data. A suitable collection of measurements can be quickly and easily performed, clearly defining which part of the space is not yet well covered by measurements. The obtained measurements, which can also be collected via the crowdsourcing approach, are used within a constrained nonlinear optimization algorithm. The latter is implemented on a smartphone and allows the online determination of the beacons’ locations and the construction of path loss models, which are validated in real-time using the particle swarm localization algorithm. The proposed system represents an advanced innovation as the application user can quickly find out when there are enough data collected for the expected radiolocation accuracy. In this way, radiolocation becomes much less time-consuming and labor-intensive as the configuration time is reduced by more than half. The experiment results show that the proposed system achieves a good trade-off in terms of network setup complexity and localization accuracy. The developed system for automated data acquisition and online modeling on a smartphone has proved to be very useful, as it can significantly simplify and speed up the installation of the Bluetooth network, especially in wide-area facilities.

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