论文标题
接近感应：建模和理解用于数字触点跟踪的嘈杂的RSSI-BLE信号和其他移动传感器数据
Proximity Sensing: Modeling and Understanding Noisy RSSI-BLE Signals and Other Mobile Sensor Data for Digital Contact Tracing
论文作者
论文摘要
当我们等待疫苗时，通过有效的接触追踪进行社会派式侵蚀已成为抑制COVID-19的主要健康策略。为了实现有效的数字接触跟踪，我们提出了一个新型系统，通过与其他智能传感器（加速度计，磁力计，陀螺仪）的蓝牙低能（BLE）信号的联合模型估算成对的个人接近度。我们探索了解释传感器数据流（时间序列，直方图等）的多种方法，并使用几种统计和深度学习方法来学习传感近距离的表示。我们报告了标准化的决策成本函数（NDCF）度量标准，并分析各种输入信号的差异影响，并讨论与此任务相关的各种挑战。
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.
