论文标题
使用蓝牙低能信号和IMU传感器读数自动接触跟踪
Automatic Contact Tracing using Bluetooth Low Energy Signals and IMU Sensor Readings
论文作者
论文摘要
在本报告中，我们将解决方案介绍了SFI机器学习中心（ML-LABS）提供的挑战，其中需要估算两部手机之间的距离。这是NIST太近太长时间的修改版本（TC4TL）挑战，因为时间方面被排除在外。我们根据蓝牙RSSI和IMU感觉数据提出了一种基于功能的方法，该方法的表现优于先前的最新水平，从而将误差降低到0.071。我们对模型进行消融研究，揭示了有关距离和蓝牙RSSI读数之间关系的有趣见解。
In this report, we present our solution to the challenge provided by the SFI Centre for Machine Learning (ML-Labs) in which the distance between two phones needs to be estimated. It is a modified version of the NIST Too Close For Too Long (TC4TL) Challenge, as the time aspect is excluded. We propose a feature-based approach based on Bluetooth RSSI and IMU sensory data, that outperforms the previous state of the art by a significant margin, reducing the error down to 0.071. We perform an ablation study of our model that reveals interesting insights about the relationship between the distance and the Bluetooth RSSI readings.
