Machine Learning Estimation of COVID-19 Social Distance using Smartphone Sensor Data

• Published in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2021; pp. 4452 - 4457
• Main Authors: Semenov, Oleksandr, Agu, Emmanuel, Pahlavan, Kaveh
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.11.2021
• Subjects: Accelerometers; Bluetooth; COVID-19; Feature extraction; Humans; Machine Learning; Machine learning algorithms; Maximum likelihood estimation; SARS-CoV-2; Sensor phenomena and characterization; Smartphone
• ISSN: 2694-0604
• DOI: 10.1109/EMBC46164.2021.9630927

Airborne infectious diseases such as COVID-19 spread when healthy people are in close proximity to infected people. Technology-assisted methods to detect proximity in order to alert people are needed. In this work we systematically investigating Machine Learning (ML) methods to detect proximity by analyzing data gathered from smartphones' built-in Bluetooth, accelerometer and gyroscope sensors. We extracted 20 statistical features from raw sensor data, which were then classified (< 6ft or not) and regressed (distance estimate) using ML algorithms. We found that elliptical filtering of accelerometer and gyroscope sensors signal improved the performance of ML regression. The most predictive features were z-axis mean and fourth momentum for the accelerometer sensors, z-axis mean y-axis mean for the gyroscope sensor, and advertiser time and mean RSSI for Bluetooth radio. After rigorous evaluation of the performance of 19 ML classification and regression methods, we found that ensemble (boosted and bagged tree) methods and regression trees ML algorithms performed best when using data from a combination of Bluetooth radio, accelerometer and the gyroscope. We were able to classify proximity (< 6ft or not) with 100% accuracy using the accelerometer sensor and with 62%-97% accuracy with the Bluetooth radio.