The growing need for technology that supports remote healthcare has been acutely highlighted by the SARS-CoV-2 (COVID-19) pandemic. A specific example of how the face of healthcare is transforming is in the number of medical appointments held via teleconference, which has increased by more than an order of magnitude because of stay-at-home orders and greater burdens on healthcare systems. Experts suggest that particular attention should be given to cardiovascular and pulmonary protection during treatment of many conditions; however, in most telehealth scenarios physicians lack access to objective measurements of a patient’s condition because of the inability to capture vital signs. I will present work focusing on methods that leverage ordinary ubiquitous sensors (e.g., webcams) to measure physiological signals (e.g., peripheral blood flow, heart rate, respiration, blood oxygenation, A.Fib., blood pressure) without contact with the body. We are developing state-of-the-art, on-device neural models and a synthetics data pipeline to help us learn more robust representations and achieve performance close to that of contact sensors.

Daniel Mcduff is a Principal Researcher at Microsoft where he leads research and development of affective technology. Daniel completed his PhD at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. Daniel’s work on non-contact physiological measurement helped to popularize a new field of low-cost health monitoring using webcams. Previously, Daniel worked at the UK MoD, was Director of Research at MIT Media Lab spin-out Affectiva and a post-doctoral research affiliate at MIT. His work has received nominations and awards from Popular Science magazine as one of the top inventions in 2011, South-by-South-West Interactive (SXSWi), The Webby Awards, ESOMAR and the Center for Integrated Medicine and Innovative Technology (CIMIT). His projects have been reported in many publications including The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist, Scientific American and Forbes magazine. Daniel was named a 2015 WIRED Innovation Fellow, an ACM Future of Computing Academy member and has spoken at TEDx and SXSW. Daniel has published over 100 peer-reviewed papers on machine learning (NeurIPS, ICLR, ICCV, ECCV, ACM TOG), human-computer interaction (CHI, CSCW, IUI) and biomedical engineering (TBME, EMBC).

Selective Comments

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