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

Authors

Suriyadeepan Ramamoorthy, Dublin City University, IrelandFollow
Joyce Mahon, University College Dublin, IrelandFollow
Michael O'Mahony, Technological University Dublin, IrelandFollow
Jean Francois Itangayenda, University College Dublin, IrelandFollow
Tendai Mukande, Dublin City University, IrelandFollow
Tlamelo Makati, Technological University Dublin, IrelandFollow

Author ORCID Identifier

0000-0003-2344-7377

Document Type

Preprint

Disciplines

Computer Sciences

Publication Details

https://arxiv.org/abs/2206.06033

doi:10.48550/arXiv.2206.06033

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.

DOI

https://doi.org/10.48550/arXiv.2206.06033

Recommended Citation

Ramamoorthy, Suriyadeepan; Mahon, Joyce; O'Mahony, Michael; Itangayenda, Jean Francois; Mukande, Tendai; and Makati, Tlamelo, "Automatic Contact Tracing using Bluetooth Low Energy Signals and IMU Sensor Readings" (2022). Other resources. 26.
https://arrow.tudublin.ie/scschcomoth/26

Funder

Science Foundation Ireland

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.