Leveraging Mobile Sensing and Bayesian Change Point Analysis to Monitor Community-scale Behavioral Interventions: A Case Study on COVID-19

During pandemics, effective interventions require monitoring the problem at different scales and understanding the various tradeoffs between efficacy, privacy, and economic burden. To address these challenges, we propose a framework where we perform Bayesian change-point analysis on aggregate behavior markers extracted from mobile sensing data collected during the COVID-19 pandemic. Results generated by 598 participants for up to four months reveal rich insights: We observe an increase in smartphone usage around February 10th, followed by an increase in email usage around February 27th and, finally, a large reduction in participant's mobility around March 13th. These behavior changes overlapped with important news events and government directives such as the naming of COVID-19, a spike in the number of reported cases in Europe, and the declaration of national emergency by President Trump. We also show that our detected change points align with changes in large scale external sources, including number of COVID-19 tweets, COVID-19 search traffic, and a large-scale foot traffic data collected by SafeGraph, providing further validation of our method. Our results show promise towards the feasibility of using mobile sensing to understand communities' responses to public health interventions. © 2022 Copyright held by the owner/author(s).

Investigating Novel Proximity Monitoring Techniques Using Ubiquitous Sensor Technology

The goal of this work is to investigate novel proximity detection techniques by researching and testing various sensor technologies and investigate their feasibility in an athletic context. COVID-19 has challenged sports teams to come up with reasonable and easy-to-implement solutions to provide a safe training environment for their players and staff. For this reason, proximity data is more important than ever, as many teams are in need of a way to measure social distancing and maintain contact tracing of their athletes. Bluetooth has been widely used to detect colocation and monitor social distancing. However, there are many other sensing technologies that may prove to be more accurate, robust, and secure. Therefore, the focus of this work is to investigate how Bluetooth compares with ultra-wideband and ultrasound technologies when monitoring the distance between users. We have implemented and compared the three modalities in a controlled experiment to investigate their accuracy at detecting distance between users at various levels. Our results indicate that the UWB signals are the most accurate at monitoring co-location. This is in-line with previous research suggesting that Bluetooth cannot accurately measure the distance between fast moving objects and needs about 20 seconds to stabilize distance measurements;therefore, it is not feasible to use for sports. In addition, we recorded that UWB models yielded an accuracy of over 95%, while ultrasound correctly classified the observations over 80% of the time, and Bluetooth had an accuracy of less than 50% when predicting if a given signal is within 6 feet or not.

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Mobile sensing systems have been widely used as a practical approach to collect behavioral and health-related information from individuals and to provide timely intervention to promote health and well-being, such as mental health and chronic care. As the objectives of mobile sensing could be either personalized medicine for individuals or public health for populations, in this work we review the design of these mobile sensing systems, and propose to categorize the design of these systems in two paradigms –(i) Personal Sensing and (ii) Crowd Sensing paradigms. While both sensing paradigms might incorporate common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and cloud-based data analytics to collect and process sensing data from individuals, we present two novel taxonomy systems based on the (a) Sensing Objectives (e.g., goals of mHealth sensing systems and how technologies achieve the goals), and (b) the Sensing Systems Design and Implementation (D&I) (e.g., designs of mHealth sensing systems and how technologies are implemented). With respect to the two paradigms and two taxonomy systems, this work systematically reviews this field. Specifically, we first present technical reviews on the mHealth sensing systems in eight common/popular healthcare issues, ranging from depression and anxiety to COVID-19. Through summarizing the mHealth sensing systems, we comprehensively survey the research works using the two taxonomy systems, where we systematically review the Sensing Objectives and Sensing Systems D&I while mapping the related research works onto the life-cycles of mHealth Sensing, i.e., (1) Sensing Task Creation &Participation, (2) Health Surveillance &Data Collection, and (3) Data Analysis &Knowledge Discovery. In addition to summarization, the proposed taxonomy systems also help the potential directions of mobile sensing for health from both personalized medicine and population health perspectives. Finally, we attempt to test and discuss the validity of our scientific approaches to the survey. IEEE

Detection and Analysis of Interrupted Behaviors by Public Policy Interventions during COVID-19

In most countries around the world, various public policies and guidelines, such as social distancing and stay-at-home orders, have been put in place to slow down the spreading of COVID-19. Relying on traditional surveys to assess policy impacts on community level behavior changes may lead to biased results, and limit fine-grained understanding of human behavior dynamics over time. We propose to leverage mobile sensing to capture people's behavior footprints amid the COVID-19 pandemic, and understand their collective behavior changes with respect to existing policies. Specifically, we propose to extract a rich set of behavioral markers from raw mobile sensing data, including mobility, social interactions, physical activities, and health states, and apply them in a generalized behavior change analysis framework to measure and detect community level behavior changes in an epidemic context. We present how to combine change point detection algorithm and interrupted time series analysis to automatically detect three different measurements of behavior changes (e.g., level, trend, and variance changes), and provide insights supported by statistical inference. A case study using a dataset that we collected from a large mobile sensing study conducted in the United States is shown to demonstrate the proposed framework and method. © 2021 IEEE.