Close-Range Indoor Proximity Detection for COVID-19 Exposure Notifications Using Smartphone Magnetometer Traces

Smartphone apps for exposure notification and contact tracing have been shown to be effective in controlling the COVID-19 pandemic. However, there is evidence that the Bluetooth Low Energy approach used for proximity detection by existing apps can be error-prone in areas with high numbers of metallic objects. In this paper, we present a new method for detecting whether or not two smartphones are 2 or fewer meters apart, intended to augment BLE-based proximity detection methods. We design a set of binary machine learning classifiers that take as input pairs of 10-second-long segments of magnetometer traces. These classifiers identify pairs of trace segments for which the two recording devices were 2 or fewer meters apart for at least 75% of the segment duration. We introduce a simple method of compensating for different magnetometer biases in heterogeneous devices. We confirm that our classifiers can generalize well to both new buildings and new devices whose traces are not present in their training data, and characterize their overall accuracy for heterogeneous-device proximity detection to be between 93.1% and 96.2%.