We live in a world where the promise of ubiquitous computing and the Internet of Things is coming true. We have smart devices that pervade our lives, and that are constantly collecting data about us and mostly discarded as irrelevant. I will demonstrate how researchers can extract relevance from this passively collected data and use it to "image" people's behaviors. I will describe approaches for extracting behavioral routines from smart devices, and then how these routines can help us better understand individual and group human behaviors, as well as anomalies. Using examples from healthcare, I will describe how we can leverage both the routines and anomalies to improve our understanding of health-related behaviors and support behavior change.
Anind K. Dey is a Professor and Dean of the Information School and Adjunct Professor in the Department of Human-Centered Design and Engineering. Anind is renowned for his early work in context-aware computing, an important theme in modern computing, where computational processes are aware of the context in which they operate and can adapt appropriately to that context. His research is at the intersection of human-computer interaction, machine learning, and ubiquitous computing. For the past few years, Anind has focused on passively collecting large amounts of data about how people interact with their phones and the objects around them, to use for producing detection and classification models for human behaviors of interest. He applies a human-centered and problem-based approach through a collaboration with an amazing collection of domain experts in areas of substance abuse (alcohol, marijuana, opioids), mental health, driving and transportation needs, smart spaces, sustainability, and education. Anind was inducted into the ACM SIGCHI Academy for his significant contributions to the field of human-computer interaction in 2015.