Ubiquitous Computing Platforms as Scalable Surrogates in Public Health and Well-being

Abstract

Computing platforms with Sensing components have become ubiquitous across modern society — present in our pockets, on our bodies, and in our environments. While these sensors were originally designed with specific purposes in mind, they can be re-contextualized to offer new insights for novel applications when integrated into controlled studies to couple them with novel signals. Because of their prevalence, these devices become an interface with the population, meaning any findings from studies including them may translate more directly to population-scale. These ubiquitous commodity devices can therefore function as a population-scale development platform for deploying accessible interventions (i.e., diagnostic resources) or extract population-scale insights (i.e., passive sensing). In this thesis, I explore three approaches to re-contextualizing or extending ubiquitous devices for new sensing applications in public health and societal well-being. First, I demonstrate how the internal temperature sensor of smartphones can be re-contextualized as a thermometer, making fever monitoring more accessible at scale. Next, I show that passively monitored biosignal trends from smartwatches can serve as early indicators of influenza infection. Finally, I show that integrating proximity sensors on bicycles can passively map a surrogate measure of road safety across the network, serving as a means of injury prevention. Through these approaches, I show how leveraging ubiquitous computing devices can make recovering public health signals more accessible, timely, convenient, and even preemptive.