A low-cost room-level indoor localization system with easy setup for medical applications

Tegou, Thomas I.; Kalamaras, Ilias; Votis, Konstantinos; Tzovaras, Dimitrios

doi:10.23919/wmnc.2018.8480912

Cited by 17 publications

(7 citation statements)

References 13 publications

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“…In this paper, a low-cost indoor localization system is proposed for monitoring the mobility behaviour of older individuals and a study is performed to assess the correlation between measured indoor activities of an older person and his/her frailty status, as assessed by well-known frailty criteria. The localization system was first presented in [3], which is hereby extended with the addition of the frailty assessment study. Preliminary results of this study, with a small dataset, were presented in [4].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed indoor localization system was firstly presented in [3] and preliminary results regarding its frailty assessment capability were presented in The rest of the paper is organized as follows: Section 2 includes related work about indoor localization and frailty monitoring, Section 3 presents the proposed localization system. Section 4 includes an evaluation of the system regarding its accuracy in room estimation and its frailty assessment capability.…”

Section: Introductionmentioning

confidence: 99%

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

Tegou

Kalamaras

Tsipouras

et al. 2019

Sensors

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tegou

Kalamaras

Tsipouras

et al. 2019

Sensors

Self Cite

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“…The closely related work is Ref. [28] which proposes the room-level accuracy for localization of Bluetooth anchors. They provide a method which is easy for a non-technical user to configure the room-level localization of anchor nodes by carrying a smartphone to collect RSSI in every room and label the RSSI data with their location (which room they collect data).…”

Section: Easy-to-deploy Smart Homementioning

confidence: 99%

EasyTrack: Zero-Calibration Smart-Home Tracking System

Kitbutrawat

Yamaguchi

Higashino

2019

Journal of Information Processing

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Location-based services in household enable not only estimating activities but also detecting the accident location of residents including children, parents and elderly people. Furthermore, home management systems make use of location information of residents for encouraging residents to live comfortably in their own homes. Passive infrared binary motion sensors are widely used in home tracking systems because of low energy consumption and flexibility on deployment. However, it's hard for non-specialists like residents to manage such location information of sensors which are very important for tracking systems. To mitigate human efforts and errors, we propose a sensor localization method to automatically identify the location of multiple binary motion sensors in a house from the observed motion detection event sequences by binary sensors. The method finds the movement patterns and characteristics of sojourn time of residents to identify the rooms where those sensors are located as well as the proximity relations among sensors assisted by prior knowledge such as from floorplan. The experimental results in actual houses show that our method can estimate the location of sensors placed close to the anchor locations within a one-day observation, and the accuracy of our approach is above 80% after three-day observation.

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“…In this case, the system estimates the location of an object by matching received signal strength indicator (RSSI) measurements with the closest location fingerprints. The RSSI measurements are often obtained from Wi-Fi access points [32] or Bluetooth beacons [33] [34] in indoor applications or Sigfox, LoRaWAN [35] or mobile communications in large areas [36]. However, these methods require a calibration of the system by recording the exact location on a map.…”

Section: Introductionmentioning

confidence: 99%

“…The main drawback of previous localization methods is that the high number of anchors and the manual calibration procedure decrease the system's return on investment (ROI). However, there are other applications in which knowing an approximate area, such as a room [34] [45] constitute an example of the second group. A major challenge for these applications is that the tracking devices must be noninvasive and private because they are intended to be used by the elderly.…”

Section: Introductionmentioning

confidence: 99%

Room-Level Localization System Based on LoRa Backscatters

2021

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The aim of this paper is to propose a novel room-level localization approach to locate LoRa backscatter devices, which can be easily embedded into wearable devices or smartphones. The advantages of this system lie in its series of low-cost, low-power, low-complexity and long-range features. LoRa backscattering operates by alternatively connecting an antenna, through a switch, to two loads with high reflection coefficients and opposite phase. The result is a frequency shift of the LoRa incident signal equal to the backscatter switching frequency. The localization system comprises several LoRa receivers distributed among the rooms, a LoRa transmitter located at a central point and the backscatter device, which is carried or worn by a subject. The position of the LoRa backscatter device can be determined by comparing the received signal strength between all receivers. In order to improve the accuracy of the system, different machine learning classifiers were compared. System performance was tested in a real-life scenario, achieving an accuracy up to 89.7% using linear discriminant analysis (LDA). INDEX TERMS Backscatter communications, LoRa, localization, wireless sensor networks, RFID, zeropower sensor, internet of things (IoT).

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A low-cost room-level indoor localization system with easy setup for medical applications

Cited by 17 publications

References 13 publications

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

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

EasyTrack: Zero-Calibration Smart-Home Tracking System

Room-Level Localization System Based on LoRa Backscatters

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