Measuring Sleep, Stress and Wellbeing with Wearable Sensors and Mobile Phone

Akane Sano, MIT

Sleep, stress and mental health have been major health issues in modern society. Poor sleep habits and high stress, as well as reactions to stressors and sleep habits, can depend on many factors. Internal factors include personality types and physiological factors and external factors include behavioral, environmental and social factors. What if 24/7 rich data from mobile devices could identify which factors influence your bad sleep or stress problem and provide personalized early warnings to help you change behaviors, before sliding from a good to a bad health condition such as depression?

In my talk, I will present a series of studies and systems we have developed at MIT to investigate how to leverage multi-modal data from mobile/wearable devices to measure, understand and improve mental wellbeing.

First, I will talk about methodology and tools I developed for the SNAPSHOT study, which seeks to measure Sleep, Networks, Affect, Performance, Stress, and Health using Objective Techniques. To learn about behaviors and traits that impact health and wellbeing, we have measured over 200,000 hours of multi-sensor and smartphone use data as well as trait data such as personality from about 300 college students exposed to sleep deprivation and high stress.

Second, I will describe statistical analysis and machine learning models to characterize, model, and forecast mental wellbeing using the SNAPSHOT study data. I will discuss behavioral and physiological markers and models that may provide early detection of a changing mental health condition.

Third, I will introduce recent projects that might help people to reflect on and change their behaviors for improving their wellbeing.

I will conclude my talk by presenting my research vision and future directions in measuring, understanding and improving mental wellbeing.

Speaker Biography

Akane Sano is a Research Scientist at MIT Media Lab, Affective Computing Group. Her research focuses on mobile health and affective computing. She has been working on measuring and understanding stress, sleep, mood and performance from ambulatory human long-term data and designing intervention systems to help people be aware of their behaviors and improve their health conditions. She completed her PhD at the MIT Media Lab in 2015. Before she came to MIT, she worked for Sony Corporation as a researcher and software engineer on wearable computing, human computer interaction and personal health care. Recent awards include the Best Paper Award at the NIPS 2016 Workshop on Machine Learning for Health and the AAAI Spring Symposium Best Presentation Award.