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

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 452 ◽  
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).

scholarly journals DSCP: Depthwise Separable Convolution-Based Passive Indoor Localization Using CSI Fingerprint

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chong Han ◽  
Wenjing Xun ◽  
Lijuan Sun ◽  
Zhaoxiao Lin ◽  
Jian Guo
Keyword(s):  
Indoor Localization ◽  
Wireless Sensing ◽  
Training Phase ◽  
Training Time ◽  
Localization Accuracy ◽  
Localization System ◽  
Complex Models ◽  
Indoor Scenarios ◽  
Trained Network ◽  
Localization Systems

Wi-Fi-based indoor localization has received extensive attention in wireless sensing. However, most Wi-Fi-based indoor localization systems have complex models and high localization delays, which limit the universality of these localization methods. To solve these problems, a depthwise separable convolution-based passive indoor localization system (DSCP) is proposed. DSCP is a lightweight fingerprint-based localization system that includes an offline training phase and an online localization phase. In the offline training phase, the indoor scenario is first divided into different areas to set training locations for collecting CSI. Then, the amplitude differences of these CSI subcarriers are extracted to construct location fingerprints, thereby training the convolutional neural network (CNN). In the online localization phase, CSI data are first collected at the test locations, and then, the location fingerprint is extracted and finally fed to the trained network to obtain the predicted location. The experimental results show that DSCP has a short training time and a low localization delay. DSCP achieves a high localization accuracy, above 97%, and a small median localization distance error of 0.69 m in typical indoor scenarios.

A Low-Cost Indoor Localization System for Energy Sustainability in Smart Buildings

2016 ◽  
Vol 16 (9) ◽  
pp. 3246-3262 ◽  
Author(s):  
Victoria Moreno ◽  
Miguel A. Zamora ◽  
Antonio F. Skarmeta
Keyword(s):  
Indoor Localization ◽  
Low Cost ◽  
Energy Sustainability ◽  
Smart Buildings ◽  
Localization System

scholarly journals Indoor Localization System Using Wi-Fi Technology

2019 ◽  
pp. 69-77
Keyword(s):  
Global Positioning System ◽  
Indoor Localization ◽  
Tracking System ◽  
Low Cost ◽  
Positioning System ◽  
Test Results ◽  
Localization System ◽  
Transmission Technique ◽  
Wide Range ◽  
Global Positioning

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.

scholarly journals Bandwidth Enhancement of a Bell-shaped UWB Antenna for Indoor Localization Systems

2021 ◽  
Vol 11 (1) ◽  
pp. 6691-6695
Author(s):  
M. S. Karoui ◽  
N. Ghariani ◽  
M. Lahiani ◽  
H. Ghariani
Keyword(s):  
Indoor Localization ◽  
Low Cost ◽  
Ground Plane ◽  
Wide Band ◽  
Impedance Bandwidth ◽  
Uwb Antenna ◽  
Simple Method ◽  
Bandwidth Enhancement ◽  
Measured Impedance ◽  
Localization Systems

In this paper, a simple method of enhancing the bandwidth of the Bell-shaped UWB Antenna for indoor localization systems is proposed. Therefore, a modified version of the bell-shaped Ultra-Wide Band (UWB) antenna for indoor localization systems is presented. The proposed antenna is printed on a low-cost FR-4 substrate of 21×27×1.6mm3 size. It is composed of a bell-shaped radiating patch and a multi-slotted ground plane. The measured results show that the proposed antenna has an impedance bandwidth of about 11.2GHz ranging from 3.16GHz to 14.36GHz at S11<−10dB. Compared to the original version, an enhancement of about 5.56GHz in the measured impedance bandwidth was observed.

scholarly journals Noise reduction for radio map crowdsourcing building in WLAN indoor localization system

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Liye Zhang ◽  
Zhuang Wang ◽  
Xiaoliang Meng ◽  
Chao Fang ◽  
Cong Liu
Keyword(s):  
Indoor Localization ◽  
Sensor Data ◽  
Mode Switching ◽  
Map Building ◽  
Position Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Straight Line ◽  
Radio Map ◽  
Localization Systems

AbstractRecent years have witnessed a growing interest in using WLAN fingerprint-based method for indoor localization system because of its cost-effectiveness and availability compared to other localization systems. In order to rapidly deploy WLAN indoor localization system, the crowdsourcing method is applied to alternate the traditional deployment method. In this paper, we proposed a fast radio map building method utilizing the sensors inside the mobile device and the Multidimensional Scaling (MDS) method. The crowdsourcing method collects RSS and sensor data while the user is walking along a straight line and computes the position information using the sensor data. In order to reduce the noise in the location space of the radio map, the short-term Fourier transform (STFT) method is used to detect the usage mode switching to improve the step determination accuracy. When building a radio map, much fewer RSS values are needed using the crowdsourcing method compared to conventional methods, which lends greater influence to noises and erroneous measurements in RSS values. Accordingly, an imprecise radio map is built based on these imprecise RSS values. In order to acquire a smoother radio map and improve the localization accuracy, the MDS method is used to infer an optimal RSS value at each location by exploiting the correlation of RSS values at nearby locations. Experimental results show that the expected goal is achieved by the proposed method.

WiCLoc: A Novel CSI-based Fingerprint Localization System

2019 ◽  
Vol 34 (04) ◽  
pp. 2058003
Author(s):  
Lei Zhang ◽  
Yanjun Hu ◽  
Yafeng Liu ◽  
Jiaxiang Li ◽  
Enjie Ding
Keyword(s):  
Nearest Neighbor ◽  
Indoor Localization ◽  
Rapid Development ◽  
Location Based Services ◽  
Smart Devices ◽  
Fingerprint Matching ◽  
Wifi Networks ◽  
Localization System ◽  
Exhibition Hall ◽  
Localization Systems

With the rapid development of smart devices and WiFi networks, WiFi-based indoor localization is becoming increasingly important in location-based services. Among various localization techniques, the fingerprint-based method has attracted much interest due to its high accuracy and low equipment requirement. Traditional fingerprint-based indoor localization systems mostly obtain positioning by measuring the received signal strength indicator (RSSI). However, the RSSI is affected by environmental influences, thereby limiting the precision of positioning. Therefore, we propose a new indoor fingerprint localization system based on channel state information (CSI). We adopt a novel method, in which the amplitude and phase of the CSI are fused to generate fingerprints in the training phase and apply a weighted [Formula: see text]-nearest neighbor (KNN) algorithm for fingerprint matching during the estimation phase. The system is validated in an exhibition hall and laboratory and we also compare the results of the proposed system with those of two CSI-based and an RSSI-based fingerprint localization systems. The results show that the proposed system achieves a minimum mean distance error of 0.85[Formula: see text]m in the exhibition hall and 1.28[Formula: see text]m in the laboratory, outperforming the other systems.

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