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Proximity Sensing Modeling and Understanding Noisy RSSI-BLE Signals and Other Mobile Sensor Data for Digital Contact Tracing

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Document pages: 6 pages

Abstract: As we await a vaccine, social-distancing via efficient contact tracing hasemerged as the primary health strategy to dampen the spread of COVID-19. Toenable efficient digital contact tracing, we present a novel system to estimatepair-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 learnrepresentations for sensing proximity. We report the normalized Decision CostFunction (nDCF) metric and analyze the differential impact of the various inputsignals, as well as discuss various challenges associated with this task.

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