Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers

by   Tom Lovett, et al.

The Covid-19 pandemic has resulted in a variety of approaches for managing infection outbreaks in international populations. One example is mobile phone applications, which attempt to alert infected individuals and their contacts by automatically inferring two key components of infection risk: the proximity to an individual who may be infected, and the duration of proximity. The former component, proximity, relies on Bluetooth Low Energy (BLE) Received Signal Strength Indicator(RSSI) as a distance sensor, and this has been shown to be problematic; not least because of unpredictable variations caused by different device types, device location on-body, device orientation, the local environment and the general noise associated with radio frequency propagation. In this paper, we present an approach that infers posterior probabilities over distance given sequences of RSSI values. Using a single-dimensional Unscented Kalman Smoother (UKS) for non-linear state space modelling, we outline several Gaussian process observation transforms, including: a generative model that directly captures sources of variation; and a discriminative model that learns a suitable observation function from training data using both distance and infection risk as optimisation objective functions. Our results show that good risk prediction can be achieved in π’ͺ(n) time on real-world data sets, with the UKS outperforming more traditional classification methods learned from the same training data.



There are no comments yet.


page 1

page 2

page 3

page 4

βˆ™ 07/20/2020

Inter-Mobile-Device Distance Estimation using Network Localization Algorithms for Digital Contact Logging Applications

Mobile applications are being developed for automated logging of contact...
βˆ™ 05/19/2020

Coronavirus Contact Tracing: Evaluating The Potential Of Using Bluetooth Received Signal Strength For Proximity Detection

We report on measurements of Bluetooth Low Energy (LE) received signal s...
βˆ™ 08/28/2020

Bluetooth-based COVID-19 Proximity Tracing Proposals: An Overview

Large-scale COVID-19 infections have occurred worldwide, which has cause...
βˆ™ 01/11/2022

Tackling Multipath and Biased Training Data for IMU-Assisted BLE Proximity Detection

Proximity detection is to determine whether an IoT receiver is within a ...
βˆ™ 01/25/2022

Improving Proximity Estimation for Contact Tracing using a Multi-channel Approach

Due to the COVID 19 pandemic, smartphone-based proximity tracing systems...
βˆ™ 06/12/2020

Distance-based phylogenetic inference from typing data: a unifying view

Typing methods are widely used in the surveillance of infectious disease...
βˆ™ 05/07/2019

PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms

Mobile phones provide a powerful sensing platform that researchers may a...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.