#5958. Feature selection for proximity estimation in COVID-19 contact tracing apps based on Bluetooth Low Energy (BLE)

July 2026publication date
Proposal available till 03-06-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Computer Networks and Communications;
Computer Science Applications;
Information Systems;
Hardware and Architecture;
Software;
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Abstract:
During the COVID-19 pandemic, contact tracing apps based on the Bluetooth Low Energy (BLE) technology found in smartphones have been deployed by multiple countries despite BLEs debatable performance for determining close contacts among users. Current solutions estimate proximity based on a single feature: the mean attenuation of the BLE signal. In this context, a new generation of these apps which better exploits data from the BLE signal and other sensors available on phones can be fostered. Collected data can be used to extract multiple features that feed machine learning models which can potentially improve the accuracy of todays solutions. In this work, we consider the use of machine learning models to evaluate different feature sets that can be extracted from the received BLE signal, and assess the performance gain as more features are introduced in these models. Since indoor conditions have a strong impact in assessing the risk of being exposed to the SARS-CoV-2, we analyze the environment (indoor or outdoor) role in these models, aiming at understanding the need for apps that could increase proximity accuracy if aware of its environment.
Keywords:
Bluetooth; Contact tracing; COVID-19; Feature selection; Machine learning; Proximity estimation

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