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SUMMARY:Pablo Paredes\, Stanford University – ““Pervasive Wellbeing Technology: Unobtrusive Sensors and Interventions for Stress Management “”
DESCRIPTION:AbstractAs society progresses towards increasing levels of embedded\, ubiquitous\, and autonomous computation\, one key societal opportunity is to leverage this technology to maximize human wellbeing. The challenge for wellbeing technology is two-fold\, how to precisely measure wellbeing\, and how to deliver long-term engaging interventions to optimize wellbeing states and their fundamental components\, such as stress. Ultimately\, managing stress\, for example\, can have significant implications in health\, wellbeing\, productivity\, and attention. The current approaches to assessing wellbeing and stress are somewhat limited\, as these assessments are based on subjective observations and they impose models of use that do not scale or adapt well to diverse populations. Additionally\, little research is done in developing human-centered intervention technology that maximizes engagement over the long term. In this talk\, I present my research agenda that focuses on unobtrusive sensing and interventions that are efficacious and engaging\, i.e.\, allowing for long-term use\, which is especially important for public health interventions. I present a series of research projects exploring and validating novel ideas on the design of passive “sensorless” sensors and subtle just-in-time personalized interventions. I show the promise of repurposing existing signals from computing peripherals (i.e.\, mouse and trackpad) or cars (steering wheel) and repurposing existing media as subtle just-in-time interventions. Finally\, inspired by biology and the behavioral sciences\, I propose we leverage technology to make “mundane” devices\, such as chairs\, desks\, cars\, or even urban lights\, into devices that deliver personalized\, adaptive\, and autonomous wellbeing interventions. I close with a brief discussion of the ethical implications and the research needed to systematically study ethics in pervasive wellbeing technology.BioPablo Paredes earned his Ph.D. in Computer Science from the University of California\, Berkeley in 2015 with Prof. John Canny. He is currently a Clinical Assistant Professor in the Psychiatry and Behavioral Sciences Department\, and the Epidemiology and Population Health Department (by courtesy) at the Stanford University School of Medicine. He leads the Pervasive Wellbeing Technology Lab\, which houses a diverse group of students from multiple departments such as computer science\, electrical engineering\, mechanical engineering\, anthropology\, neuroscience\, and linguistics. Prior to joining the School of Medicine\, Dr. Paredes was a Postdoctoral Researcher in the Computer Science Department at Stanford University with Prof. James Landay. During his Ph.D. career\, he held internships on behavior change and affective computing at Microsoft Research and Google. He has been an active associate editor for the Interactive\, Mobile\, Wireless\, and Ubiquitous Technology Journal (IMWUT)\, as well as a reviewer and editor for multiple top CS and medical journals. Before 2010\, he was a senior strategic manager with Intel in Sao Paulo\, Brazil\, a lead product manager with Telefonica in Quito\, Ecuador\, and an entrepreneur in his native Ecuador and more recently in the US. In these roles\, he has had the opportunity to hire and closely evaluate designers\, engineers\, business people\, and researchers in telecommunications and product development. During his academic career\, Dr. Paredes has advised close to 40 mentees including postdocs\, Ph.D.\, master’s\, and undergraduate students\, collaborated with colleagues from multiple departments across engineering\, medicine\, and the humanities\, and raised funding from NSF\, NIH\, and large multidisciplinary intramural research projects.HostMichael DinitzVideoWatch seminar video.
URL:https://www.cs.jhu.edu/event/pablo-paredes-stanford-university-pervasive-wellbeing-technology-unobtrusive-sensors-and-interventions-for-stress-management/
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