Location: Maths Lecture Theatre D, University of St Andrews
In the UK, more than 70% of mobile users now own a smartphone. These increasingly powerful, sensor-rich, and personal devices present an immense opportunity to monitor health-related behaviours and deliver digital behaviour-change interventions at unprecedented scale.
However, designing and building systems to measure and intervene on health behaviours presents a number of challenges. These range from balancing between energy efficiency and data granularity, translating between behavioural theory and design, making long psychological assessments usable for end users, and making sense of the sensor and survey data these apps collect in a multidisciplinary setting.
Approximately 18 months ago, we launched Emotion Sense, a mood-tracking app for Android where we tried to address some of these challenges. To date, the app has been downloaded over 35,000 times and has an active user base of about 2,000 people: in this talk, I will describe how we designed, trialled, and launched Emotion Sense, and the insights we are obtaining about diurnal patterns of activity and happiness that we are finding by mining the 100 million+ accelerometer samples the app has collected to date. I’ll close with future directions of this technology — including a novel smoking cessation intervention (Q Sense), and a generic platform (Easy M) that we have developed to allow researchers to conduct their own studies.
Neal is a Senior Research Associate in Cambridge University’s Computer Laboratory. His research to date falls somewhere in the intersection of data mining, mobile systems, ubiquitous/pervasive systems, and personalisation/ recommender systems, applied to a variety of contexts where we measure human behaviour by their digital footprints. He has a PhD in Computer Science from University College London. More info/contact http://www.cl.cam.ac.uk/~nkl25/
This seminar is part of our ongoing series from researchers in HCI. See here for our current schedule.