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.