Self-Calibrated UWB based device-free indoor localization and activity detection approach

Bregar, Klemen; Hrovat, Andrej; Mohorčič, Mihael; Javornik, Tomaž

doi:10.1109/eucnc48522.2020.9200968

Cited by 21 publications

(15 citation statements)

References 11 publications

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“…The system is adaptable to specific service requirements and was tested in the laboratory and in elderly people’s homes. In [ 55 ], a self-calibrated UWB DFL and activity detection approach is presented. The automatic system setup is performed by the multidimensional scaling (MDS) algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…It coordinates the communication activities by sending beacons containing requests for slave devices. UWB radios in standalone devices acting as slave devices predominantly listen for beacons received from the master device and respond accordingly [ 55 ]. A typical pilot deployment in SAAM consists of two standalone UWB devices and one EGW device.…”

Section: Al Motion Detection Systemmentioning

confidence: 99%

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UWB Radio-Based Motion Detection System for Assisted Living

Bregar

Hrovat

Mohorčič

2021

Sensors

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Because of the ageing population, the demand for assisted living solutions that can help prolonging independent living of elderly at their homes with reduced interaction with caregivers is rapidly increasing. One of the most important indicators of the users’ well-being is their motion and mobility inside their homes, used either on its own or as contextual information for other more complex activities such as cooking, housekeeping or maintaining personal hygiene. In monitoring users’ mobility, radio frequency (RF) communication technologies have an advantage over optical motion detectors because of their penetrability through the obstacles, thus covering greater areas with fewer devices. However, as we show in this paper, RF links exhibit large variations depending on channel conditions in operating environment as well as the level and intensity of motion, limiting the performance of the fixed motion detection threshold determined on offline or batch measurement data. Thus, we propose a new algorithm with an online adaptive motion detection threshold that makes use of channel impulse response (CIR) information of the IEEE 802.15.4 ultra-wideband (UWB) radio, which comprises an easy-to-install robust motion detection system. The online adaptive motion detection (OAMD) algorithm uses a sliding window on the last 100 derivatives of power delay profile (PDP) differences and their statistics to set the threshold for motion detection. It takes into account the empirically confirmed observation that motion manifests itself in long-tail samples or outliers of PDP differences’ probability density function. The algorithm determines the online threshold by calculating the statistics on the derivatives of the 100 most recent PDP differences in a sliding window and scales them up in the suitable range for PDP differences with multiplication factors defined by a data-driven process using measurements from representative operating environments. The OAMD algorithm demonstrates great adaptability to various environmental conditions and exceptional performance compared to the offline batch algorithm. A motion detection solution incorporating the proposed highly reliable algorithm can complement and enhance various assisted living technologies to assess user’s well-being over long periods of time, detect critical events and issue warnings or alarms to caregivers.

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Section: Related Workmentioning

confidence: 99%

Section: Al Motion Detection Systemmentioning

confidence: 99%

UWB Radio-Based Motion Detection System for Assisted Living

Bregar

Hrovat

Mohorčič

2021

Sensors

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“…An alternative approach for an experimental estimation of CIR is using off-the-shelf radio equipment, which supports the estimation and recording CIR. For example, the radio equipment based on the IEEE Standard for Local and Metropolitan Area Networks [66], using ultra-wideband (UWB) communication technology supports mechanisms to estimate the CIR from a transmitted training sequence [67][68][69].…”

Section: Radio-environment Acquisition Modulementioning

confidence: 99%

Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials

et al. 2021

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Available digital maps of indoor environments are limited to a description of the geometrical environment, despite there being an urgent need for more accurate information, particularly data about the electromagnetic (EM) properties of the materials used for walls. Such data would enable new possibilities in the design and optimization of wireless networks and the development of new radio services. In this paper, we introduce, formalize, and evaluate a framework for machine learning (ML) based wireless sensing of indoor surface materials in the form of EM properties. We apply the radio-environment (RE) signatures of the wireless link, which inherently contains environmental information due to the interaction of the radio waves with the environment. We specify the content of the RE signature suitable for surface-material classification as a set of multipath components given by the received power, delay, phase shift, and angle of arrival. The proposed framework applies an ML approach to construct a classification model using RE signatures labeled with the environmental information. The ML method exploits the data obtained from measurements or simulations. The performance of the framework in different scenarios is evaluated based on standard ML performance metrics, such as classification accuracy and F-score. The results of the elementary case prove that the proposed approach can be applied for the classification of the surface material for a plain environment, and can be further extended for the classification of wall materials in more complex indoor environments.

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“…Wang et al propose UWB sensors that measure the received signal strength variation based on CIR measurements for device-free localization [ 11 ]. In [ 12 ], Bregar et al deploy a radio tomographic imaging-based DFL system with UWB radios. They measure the distance from the nodes with time-of-flight measurements and the DFL system determines the value of the first CIR peak.…”

Section: Related Workmentioning

confidence: 99%

MAMPI-UWB—Multipath-Assisted Device-Free Localization with Magnitude and Phase Information with UWB Transceivers

Cimdins

Schmidt

Hellbrück

2020

Sensors

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In this paper, we propose a multipath-assisted device-free localization (DFL) system that includes magnitude and phase information (MAMPI). The DFL system employs ultra-wideband (UWB) channel impulse response (CIR) measurements, enabling the extraction of several multipath components (MPCs) and thereby benefits from multipath propagation. We propose a radio propagation model that calculates the effect on the received signal based on the position of a person within a target area. Additionally, we propose a validated error model for the measurements and explain the creation of different feature vectors and extraction of the MPCs from Decawave DW1000 CIR measurements. We evaluate the system via simulations of the position error probability and a measurement setup in an indoor scenario. We compare the performance of MAMPI to a conventional DFL system based on four sensor nodes that measures radio signal strength values. The combination of the magnitude and phase differences for the feature vectors results in a position error probability that is comparable to a conventional system but requires only two sensor nodes.

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Self-Calibrated UWB based device-free indoor localization and activity detection approach

Cited by 21 publications

References 11 publications

UWB Radio-Based Motion Detection System for Assisted Living

UWB Radio-Based Motion Detection System for Assisted Living

Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials

MAMPI-UWB—Multipath-Assisted Device-Free Localization with Magnitude and Phase Information with UWB Transceivers

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