Automatic Contact Tracing using Bluetooth Low Energy Signals and IMU Sensor Readings

TL;DR

This paper introduces a feature-based method combining Bluetooth RSSI and IMU data for contact tracing, significantly improving distance estimation accuracy over previous approaches.

Contribution

It presents a novel approach that outperforms prior methods in estimating phone-to-phone distance using combined Bluetooth and IMU sensor data.

Findings

Error reduced to 0.071 in distance estimation

Bluetooth RSSI and IMU data relationship analyzed

Model outperforms previous state-of-the-art methods

Abstract

In this report, we present our solution to the challenge provided by the SFI Centre for Machine Learning (ML-Labs) in which the distance between two phones needs to be estimated. It is a modified version of the NIST Too Close For Too Long (TC4TL) Challenge, as the time aspect is excluded. We propose a feature-based approach based on Bluetooth RSSI and IMU sensory data, that outperforms the previous state of the art by a significant margin, reducing the error down to 0.071. We perform an ablation study of our model that reveals interesting insights about the relationship between the distance and the Bluetooth RSSI readings.