Tuesday 12pm, 1 December 2015

Pablo image

Pervasive well-being technology

Pablo Paredes

PhD Candidate - UC Berkeley EECS


Well-being is defined as the characteristic of humans to feel well. This concept is based mostly in obtaining an optimal mental health state. Newer research has showed that appropriate stress management, an increase in positive emotions and empathic social interaction generates improvements not only in mental and affective states, but even in physical ones. However, the only way to reach well-being is by maintaining a healthy lifestyle, which implies that technologies that support this goal should measure and intervene "life" itself!

The good news is that, with the advent and adoption of the Internet of Things (IoT) and Big Data technologies, and the fast growth of novel fields such as affective computing and critical engineering, we are in a unique position to challenge the way we think of technology with regards to its use towards well-being. With so many opportunities to sense and consume data we can evolve from a reactive diagnostic health approach towards one that takes advantage of pervasive technology to foster good habits, resilience and personal growth.

In this talk I will discuss a group of technologies that I encapsulate in two parts: "Sensor-less Sensing" and "Opportunistic Interventions". These new design perspectives present novel ways to repurpose current data streams to be able to measure and investigate human traits and trends as they relate to mental and physical health. I will discuss some of the ideas behind these concepts, which leverage opportunistic use of technology that has already survived the chasm of adoption. Furthermore I will provide a perspective on how we could challenge the way traditional and newer pervasive technologies, such as cars, computers, urban lighting, the web, mobile and wearable devices are used to transform them into key enablers of well-being.


Pablo is a PhD Candidate in Electrical Engineering and Computer Science at Berkeley, where he focuses on well-being, behavior change and technology. His dissertation focuses on stress sensing, intervention and management using smartphone sensors and other common technologies. Pablo has worked at Microsoft Research, Google and Intel and most recently taught "d.compress: Designing Calm" at Stanford's, where students tested unconventional product designs to reduce stress and cultivate better work/life balance. Pablo grew up in Ecuador and received his Master's in Electrical Engineering and Computer Science on a Fulbright scholarship at the Georgia Institute of Technology.