PROXIMITY DETECTION IN CONTACT TRACING WITH BLUETOOTH TECHNOLOGY DURING COVID19 PANDEMIC
by Alice Feng
Abstract – The global pandemic COVID19 is spreading through close contact between an infected person and another person. Contact tracing is a useful tool in preventing the spread of the pandemic. Contact tracing identifies infected patients and their contacts who may have been exposed and asks them to quarantine. To automate contact tracing, Bluetooth Low Energy (BLE) technology is one option to identify the distance between two objects by correlating the attenuation of BLE signal strength with distance. Hence it can be used to automatically record how close two persons are and for how long. In this project, we studied the feasibility of correctly identifying the distance between two indoor objects using Raspberry Pi’s and BLE technology. We conducted extensive experiments, correlated BLE signal strength attenuation with distance, and designed a simple lowpass filtering algorithm which optimized regression fitting and proximity decision, achieving accuracy over 90%. We also studied the impact of human obstruction, multipath, humidity, temperature, and down-sampling on proximity detection.