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

Abstract

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.

CSI-Based Proximity Estimation: Data-Driven and Model-Based Approaches

“…Correlation-based approaches have been investigated using Bluetooth [29], [30], WiFi [31], [32], and cellular networks [2]. In [31], the authors follow a purely model-based approach by applying a linear classifier on WiFi CSI subcarrier amplitude correlation and covariance, reaching up to 98% accuracy when classifying individuals as within 2 m or not.…”

“…However, their goal is to detect motion proximity; their study does not cover static individuals. In [30], the authors employ both the beacon-and the correlationbased strategies. In addition to D2D Bluetooth beacons, smartphone microphones were used to build ambient sound amplitude histograms, which are compared between phones using the Kullback-Leibler divergence.…”

The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey

“…Bluetooth technology is another RF approach to perform HAR tasks. However, compared with the WiFi signal, the Bluetooth signal is relatively weak [63]. Thus the accuracy and reaching range is limited.…”

Relative-Position Estimation Based on Loosely Coupled UWB–IMU Fusion for Wearable IoT Devices

“…Establishing the relative positions of objects is critical for various collaborative applications, including robot movement tracking, virtual/augmented reality (VR/AR), line following, and autonomous vehicles. Establishing the relative positioning between two objects is usually dependent on the deployment of fixed or mobile anchors based on Bluetooth [1], Wi-Fi [2], Global Positioning System (GPS) [3], or ultra-wideband (UWB) radio technology [4]. Most of the solutions applied in relative-position systems use range-measurement techniques.…”