, , Douglas A. Reynolds
One of the keys to managing the current (and future) epidemic is notifying people of possible virus exposure so they can isolate and seek treatment to limit further spread of the disease. While manual contact tracing is effective for notifying those who may have been exposed, it is believed that automated exposure notification will be a necessary addition as societies open up. Current approaches to automated exposure notification rely on using Bluetooth Low Energy (BLE) signals (or chirps) from smartphones to detect if a person has been too close for too long (TC4TL) to an infected individual. However, the received signal strength indicator (RSSI) value of Bluetooth chirps sent between phones is a very noisy estimator of the actual distance between the phones and can be dramatically affected in real-world conditions by i) where the phones are carried, ii) body positions, ii) physical barriers, and iv) multi-path environments, to mention a few. To better characterize the effectiveness of range and time estimation using the BLE signal, many research organizations around the world are collecting Bluetooth handshake data as well as other phone sensor data (e.g., accelerometer, gyroscope, proximity) between various types of phones with simulated real-world variability. The best hope for a solution to this difficult and important problem is to leverage the world-wide research community with common tasks, data, and success metrics that allow for the exchange of and building on collective ideas and approaches.
NIST TC4TL Challenge
Bluetooth Low Energy, Exposure Notification, TC4TL detection, NIST evaluation