Detecting human encounters from WiFi radio signals

Abstract

We present the design, implementation and evaluation of a novel human encounter detection framework for measuring and analysing human behaviour in social settings. We propose the use of WiFi probes, management frames of WiFi, that periodically radiate from mobile devices (as proxies for humans), and existing WiFi access points to automatically capture radio signals and detect human copresence. Based on the spatio-temporal properties of this copresence and their interplay we defined a model, borrowing theories from sociology, to detect human encounters -- short-lived, spontaneous human interactions. We evaluated our framework using controlled and in-the-wild experiments yielding a detection performance of 96% and 86% respectively. As such, our framework opens up interesting opportunities for designing proxemic and group applications, as well as conducting large-scale studies in the areas of computational social sciences.