IMS Closing Session

IMS Closing Session and Reception: Health Monitoring with Machine Learning and Wireless Sensors

Thursday, 6 June 2019
BCEC, Grand Ballroom
15:30 - 18:00

Dina Katabi, Andrew & Erna Viterbi Professor of Electrical Engineering and Computer Science, MIT

Abstract: Driven by advances in medicine and increased lifespans, societies are now aging at an alarming rate. This fact presents a host of new challenges - many seniors live alone and are subject to falls, accidental injuries, chronic disease exacerbations, and depression. The situation places an alarming burden on our health care system and society more generally, a burden that is only expected to grow over time.
This talk will introduce Emerald, a new technology that uses machine learning for health monitoring in the home. Emerald automates health monitoring through innovations in wireless sensing and machine learning. The Emerald device is a Wi-Fi like box that transmits low power radio signals, and analyzes their reflections using neural networks. It infers the movements, breathing, heart rate, falls, sleep apnea, and sleep stages, of people in the home -- all without requiring them to wear any sensors or wearables. By monitoring a variety of physiological signals continuously and without imposing a burden on users, Emerald can automatically detect degradation in health, enabling early intervention and care. The talk will describe the underlying technology, and present results demonstrating Emerald's promise in a geriatric population.

 

About the Speaker: Dina Katabi is the Andrew & Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT. She is also the director of the MIT’s Center for Wireless Networks and Mobile Computing, a member of the National Academy of Engineering, and a recipient of the MacArthur Genius Award.  Professor Katabi received her PhD and MS from MIT in 2003 and 1999, and her Bachelor of Science from Damascus University in 1995.  Katabi's research focuses on innovative mobile and wireless technologies with application to digital health.  Her research has been recognized with ACM Prize in Computing, the ACM Grace Murray Hopper Award, the SIGCOMM test of Time Award, the Faculty Research Innovation Fellowship, a Sloan Fellowship, the NBX Career Development chair, and the NSF CAREER award. Her students received the ACM Best Doctoral Dissertation Award in Computer Science and Engineering twice. Further, her work was recognized by the IEEE William R. Bennett prize, three ACM SIGCOMM Best Paper awards, an NSDI Best Paper award, and a TR10 award. Several start-ups have been spun out of Katabi's lab.