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

Speaker: Akane Sano , MIT Media Lab

Date: Tuesday, March 07, 2017

Time: 1:00 PM to 2:00 PM Note: all times are in the Eastern Time Zone

Refreshments: 12:45 PM

Public: Yes

Location: Seminar Room G449 (Patil/Kiva)

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Host: Stefanie Mueller, MIT CSAIL

Contact: Amy Xian Zhang, axz@csail.mit.edu

Relevant URL: http://web.media.mit.edu/~akanes/

Speaker URL: None

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Reminders to: seminars@csail.mit.edu, hci-seminar@csail.mit.edu, chi-labs@csail.mit.edu, msgs@media.mit.edu

Reminder Subject: TALK: Measuring Sleep, Stress and Wellbeing with Wearable Sensors and Mobile Phones

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 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 we 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 future directions in measuring,
understanding and improving mental wellbeing.

Bio

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 AAAI Spring Symposium Best Presentation Award and
MIT Global Fellowship.

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Created by Amy Xian Zhang at Friday, March 03, 2017 at 2:56 AM.