Abstract
The recent pandemic revealed weaknesses in several areas, including the limited capacity of public health systems for efficient case tracking and reporting. In the post-pandemic era, it is essential to be ready and provide not only preventive measures, but also effective digital strategies and solutions to protect our population from future outbreaks. This work presents a contact tracing solution based on wearable devices to track epidemic exposure. Our proximity-based privacy-preserving contact tracing (P3CT) integrates: 1) the Bluetooth Low Energy (BLE) technology for reliable proximity sensing, 2) a machine-learning approach to classify the exposure risk of a user, and 3) an ambient signature protocol for preserving the user’s identity. Proximity sensing exploits the signals emitted from a smartwatch to estimate users’ interaction, in terms of distance and duration. Supervised learning is then used to train four classification models to identify the exposure risk of a user with respect to a patient diagnosed with an infectious disease. Finally, our proposed P3CT protocol uses ambient signatures to anonymize the infected patient’s identity. Extensive experiments demonstrate the feasibility of our proposed solution for real-world contact tracing problems. The large-scale dataset consisting of the signal information collected from the smartwatch is available online. According to experimental results, wearable devices along with machine learning models are a promising approach for epidemic exposure notification and tracking.
Highlights
T HE viral spreading of the highly contagious virus, such as COVID-19, has suspended many business operations in correspondence to the government’s lockdown order to contain the pandemic
Motivated by the limitation of the smartphone-based approach in facilitating contact tracing in a work environment, this paper proposes a wearable contact tracing solution based on a low-cost smartwatch, namely proximity-based privacy-preserving contact tracing (P3CT)
Contact tracing is deemed to be an essential measure in the post-pandemic to prevent the second outbreak while slowly reopening the workplace
Summary
T HE viral spreading of the highly contagious virus, such as COVID-19, has suspended many business operations in correspondence to the government’s lockdown order to contain the pandemic. In contrast to most contact tracing solutions that identify a high-risk user (i.e., the user who is most likely to contract the virus) based on the proximity information estimated from the given RSS values, our proposed P3CT identifies the highrisk user by jointly considering the interaction range and interaction duration when any two users come into closed proximity. This is inspired by the fact that, according to epidemiologists, the exposure risk is low if the user spent less than 1 s in close proximity to the infected patient, comparing to the user who spent more than 1 hr in not so close proximity, yet still relatively near (i.e., the smartwatch still in the broadcasting range), to the infected patient [14].
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