A wearable magnetic field based proximity sensing system for monitoring COVID-19 social distancing

Bian, Sizhen; Zhou, Bo; Bello, Hymalai; Lukowicz, Paul

doi:10.1145/3410531.3414313

Cited by 49 publications

(33 citation statements)

References 8 publications

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“…The work presented by Bian and colleagues involves designing a wearable, oscillating magnetic field-based proximity sensing system to monitor the social distances between people. The authors developed a small-size node that was able to detect the distance of the persons approaching in the range of (1.5-2.0 m), where the developed nodes were evaluated in controlled lab and in real-life large store area [3]. The proposed system achieves a detection range beyond 2 m and is robust enough for everyday environments.…”

Section: Related Workmentioning

confidence: 99%

Social Distance Monitoring Approach Using Wearable Smart Tags

Alhmiedat

Aborokbah

2021

Electronics

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Coronavirus has affected millions of people worldwide, with the rate of infected people still increasing. The virus is transmitted between people through direct, indirect, or close contact with infected people. To help prevent the social transmission of COVID-19, this paper presents a new smart social distance system that allows individuals to keep social distances between others in indoor and outdoor environments, avoiding exposure to COVID-19 and slowing its spread locally and across the country. The proposed smart monitoring system consists of a new smart wearable prototype of a compact and low-cost electronic device, based on human detection and proximity distance functions, to estimate the social distance between people and issue a notification when the social distance is less than a predefined threshold value. The developed social system has been validated through several experiments, and achieved a high acceptance rate (96.1%) and low localization error (<6 m).

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Section: Related Workmentioning

confidence: 99%

Social Distance Monitoring Approach Using Wearable Smart Tags

Alhmiedat

Aborokbah

2021

Electronics

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“…In these cases it is helpful to support social distancing and contract tracing by technically aided approaches [7]. There are a series of sensing modalities with distance or proximity monitoring as a basic functionality, such as ultrasound [8][9][10], Ultra Wide Band (UWB) [11][12][13], magnetic field [14,15], electric field [16], visual-based sensing [17] etc. Ultrasound and UWB are wave-based active approaches that would be able to deliver distance information with a very high accuracy but suffer from the large amount of deployment work, and are only suitable for indoor distance monitoring.…”

Section: Related Workmentioning

confidence: 99%

Using Privacy Respecting Sound Analysis to Improve Bluetooth Based Proximity Detection for COVID-19 Exposure Tracing and Social Distancing

Bahle

Rey

Bian

et al. 2021

Sensors

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We propose to use ambient sound as a privacy-aware source of information for COVID-19-related social distance monitoring and contact tracing. The aim is to complement currently dominant Bluetooth Low Energy Received Signal Strength Indicator (BLE RSSI) approaches. These often struggle with the complexity of Radio Frequency (RF) signal attenuation, which is strongly influenced by specific surrounding characteristics. This in turn renders the relationship between signal strength and the distance between transmitter and receiver highly non-deterministic. We analyze spatio-temporal variations in what we call “ambient sound fingerprints”. We leverage the fact that ambient sound received by a mobile device is a superposition of sounds from sources at many different locations in the environment. Such a superposition is determined by the relative position of those sources with respect to the receiver. We present a method for using the above general idea to classify proximity between pairs of users based on Kullback–Leibler distance between sound intensity histograms. The method is based on intensity analysis only, and does not require the collection of any privacy sensitive signals. Further, we show how this information can be fused with BLE RSSI features using adaptive weighted voting. We also take into account that sound is not available in all windows. Our approach is evaluated in elaborate experiments in real-world settings. The results show that both Bluetooth and sound can be used to differentiate users within and out of critical distance (1.5 m) with high accuracies of 77% and 80% respectively. Their fusion, however, improves this to 86%, making evident the merit of augmenting BLE RSSI with sound. We conclude by discussing strengths and limitations of our approach and highlighting directions for future work.

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“…20 Bian et al 20 presented a system and first results of an evaluation study for monitoring COVID-19 social distancing based on a wearable magnetic-field-based proximity sensing approach. 21…”

Section: Health and Assistive Technologymentioning

confidence: 99%