Cargando…
Using Privacy Respecting Sound Analysis to Improve Bluetooth Based Proximity Detection for COVID-19 Exposure Tracing and Social Distancing
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 compl...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8402400/ https://www.ncbi.nlm.nih.gov/pubmed/34451046 http://dx.doi.org/10.3390/s21165604 |
_version_ | 1783745781009743872 |
---|---|
author | Bahle, Gernot Fortes Rey, Vitor Bian, Sizhen Bello, Hymalai Lukowicz, Paul |
author_facet | Bahle, Gernot Fortes Rey, Vitor Bian, Sizhen Bello, Hymalai Lukowicz, Paul |
author_sort | Bahle, Gernot |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8402400 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84024002021-08-29 Using Privacy Respecting Sound Analysis to Improve Bluetooth Based Proximity Detection for COVID-19 Exposure Tracing and Social Distancing Bahle, Gernot Fortes Rey, Vitor Bian, Sizhen Bello, Hymalai Lukowicz, Paul Sensors (Basel) Article 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. MDPI 2021-08-20 /pmc/articles/PMC8402400/ /pubmed/34451046 http://dx.doi.org/10.3390/s21165604 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bahle, Gernot Fortes Rey, Vitor Bian, Sizhen Bello, Hymalai Lukowicz, Paul Using Privacy Respecting Sound Analysis to Improve Bluetooth Based Proximity Detection for COVID-19 Exposure Tracing and Social Distancing |
title | Using Privacy Respecting Sound Analysis to Improve Bluetooth Based Proximity Detection for COVID-19 Exposure Tracing and Social Distancing |
title_full | Using Privacy Respecting Sound Analysis to Improve Bluetooth Based Proximity Detection for COVID-19 Exposure Tracing and Social Distancing |
title_fullStr | Using Privacy Respecting Sound Analysis to Improve Bluetooth Based Proximity Detection for COVID-19 Exposure Tracing and Social Distancing |
title_full_unstemmed | Using Privacy Respecting Sound Analysis to Improve Bluetooth Based Proximity Detection for COVID-19 Exposure Tracing and Social Distancing |
title_short | Using Privacy Respecting Sound Analysis to Improve Bluetooth Based Proximity Detection for COVID-19 Exposure Tracing and Social Distancing |
title_sort | using privacy respecting sound analysis to improve bluetooth based proximity detection for covid-19 exposure tracing and social distancing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8402400/ https://www.ncbi.nlm.nih.gov/pubmed/34451046 http://dx.doi.org/10.3390/s21165604 |
work_keys_str_mv | AT bahlegernot usingprivacyrespectingsoundanalysistoimprovebluetoothbasedproximitydetectionforcovid19exposuretracingandsocialdistancing AT fortesreyvitor usingprivacyrespectingsoundanalysistoimprovebluetoothbasedproximitydetectionforcovid19exposuretracingandsocialdistancing AT biansizhen usingprivacyrespectingsoundanalysistoimprovebluetoothbasedproximitydetectionforcovid19exposuretracingandsocialdistancing AT bellohymalai usingprivacyrespectingsoundanalysistoimprovebluetoothbasedproximitydetectionforcovid19exposuretracingandsocialdistancing AT lukowiczpaul usingprivacyrespectingsoundanalysistoimprovebluetoothbasedproximitydetectionforcovid19exposuretracingandsocialdistancing |