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COVID-19 and Your Smartphone: BLE-Based Smart Contact Tracing.

Pai Chet Ng¹, Petros Spachos², Konstantinos N Plataniotis³

¹Department of Electronics and Computer EngineeringHong Kong University of Science and Technology Hong Kong.

IEEE Systems Journal

|May 18, 2022

Summary

Smart contact tracing (SCT) uses Bluetooth signals and machine learning to quickly identify infectious disease contacts. A decision tree classifier achieved 90% accuracy, enhancing public health response while preserving user privacy.

Keywords:

Bluetooth low energy COVID-19 contact tracing physical distancing proximity smartphone

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Area of Science:

Epidemiology
Computer Science
Public Health

Background:

Manual contact tracing is slow and inefficient for infectious disease control.
Timely identification of contacts is crucial to prevent disease spread.
Existing digital solutions often face privacy concerns.

Purpose of the Study:

To propose a Smart Contact Tracing (SCT) system using Bluetooth low energy and machine learning.
To classify contacts as high/low-risk through precise proximity sensing.
To ensure user anonymity via a privacy-preserving communication protocol.

Main Methods:

Utilized smartphone Bluetooth low energy signals for proximity sensing.
Employed machine learning classifiers, including decision trees, for risk assessment.
Implemented a privacy-preserving protocol for secure data storage and dissemination.
Collected a dataset of approximately 123,000 data points from six experiments.

Main Results:

A decision tree classifier achieved approximately 90% accuracy in identifying contacts.
The SCT system provides real-time alerts for social distancing violations.
The system demonstrated effective user anonymity and privacy protection.
Publicly released a comprehensive dataset to facilitate further research.

Conclusions:

Smart Contact Tracing (SCT) offers an efficient and accurate method for infectious disease control.
The proposed system balances effective contact tracing with robust user privacy.
Machine learning, particularly decision trees, significantly enhances contact tracing accuracy.
The publicly available dataset supports ongoing research and development in digital epidemiology.