This event has ended. Visit the official site or create your own event on Sched.
Back To Schedule
Friday, October 6 • 1:15pm - 2:00pm
The Science of Sweet Dreams: Predicting Sleep Efficiency from Wearable Device Data

Log in to save this to your schedule, view media, leave feedback and see who's attending!

Feedback form is now closed.

Sleep research is a translational science field, and can greatly benefit from improved computational technologies.  Automated, robust, interpretable, and high-fidelity models are vital to its success. The pervasive adoption of wearable devices provides a unique opportunity.  Wearable devices monitor a user for an extended period of time and can generate a large amount of data. The current sleep analysis processes are manual and unable to scale, creating a bottleneck for sleep research. Automation allows for immediate analysis on large-scale clinical trials, and provides a platform for affordable widespread population screening of sleep disorders. Particularly for population screening, it is critical that the methods and knowledge extracted are generalizable and thus robust to noise and variance amongst sub-populations.  For example, teenagers follow very different sleep patterns compared to the elderly. Moreover, sleep experts use software tools such as ActiLife to manually annotate datasets.  This leaves the data interpretation prone to human error.

This talk presents a translational science approach to human health through sleep analysis, creating novel state-of-the-art computer science algorithms to empower clinicians and patients alike. We introduce the basics of sleep science, highlighting the computing challenges in the field, and proposing computational solutions to improve the sleep science process. By providing tools for sleep and activity behavior analysis, clinical decision- makers can deliver improved and informed healthcare.  Moreover, this research also empowers patients from a quantified-self perspective, by conveying real-time recommendations to optimize productivity and improve quality of life.

avatar for Jaideep Srivastava

Jaideep Srivastava

Professor, University of Minnesota CSE
Jaideep Srivastava is Professor of Computer Science at the University of Minnesota, where he directs a laboratory focusing on research in Web Mining, Social Analytics, and Health Analytics. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), has been an... Read More →

Friday October 6, 2017 1:15pm - 2:00pm CDT
Room 4 & 5 Optum, 13625 Technology Drive, Eden Prairie, MN, United States