Proximity Sensing: Modeling and Understanding Noisy RSSI-BLE Signals and Other Mobile Sensor Data for Digital Contact Tracing
This addresses the need for efficient contact tracing during the COVID-19 pandemic, but it is incremental as it builds on existing sensor-based methods.
The paper tackled the problem of estimating individual proximity for digital contact tracing by developing a system that jointly models Bluetooth Low Energy signals with other mobile sensors, achieving results measured with the normalized Decision Cost Function (nDCF) metric.
As we await a vaccine, social-distancing via efficient contact tracing has emerged as the primary health strategy to dampen the spread of COVID-19. To enable efficient digital contact tracing, we present a novel system to estimate pair-wise individual proximity, via a joint model of Bluetooth Low Energy (BLE) signals with other on-device sensors (accelerometer, magnetometer, gyroscope). We explore multiple ways of interpreting the sensor data stream (time-series, histogram, etc) and use several statistical and deep learning methods to learn representations for sensing proximity. We report the normalized Decision Cost Function (nDCF) metric and analyze the differential impact of the various input signals, as well as discuss various challenges associated with this task.