Imaging-based, non-contact measurement of physiology (including imaging photoplethysmography and imaging ballistocardiography) is a growing field of research. There are several strengths of imaging methods that make them attractive. They remove the need for uncomfortable contact sensors and can enable spatial and concomitant measurement from a single sensor. Furthermore, cameras are ubiquitous and often low-cost solutions for sensing. I will present an overview of the field and some of the latest work leveraging deep convolutional neural networks for non-contact image-based physiological measurement and magnification. These networks produce state-of-the-art results, especially in the presence of large body motions. I will then show how these techniques can be used to gather large scale and longitudinal cardiac measurements. I'll show how other image-based sensing can be used to complement these measurements to help us understand diurnal and circadian patterns in physiological states and emotions.

Daniel McDuff (http://alumni.media.mit.edu/~djmcduff/) is a Researcher at Microsoft where he leads research and development of affective computing technology, with a focus on scalable tools to enable the automated recognition and analysis of emotions and physiology. Daniel completed his PhD in the Affective Computing Group at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. Previously, Daniel was Director of Research at Affectiva and a post-doctoral research affiliate at the MIT Media Lab. During his Ph.D. and at Affectiva he built state-of-the-art facial expression recognition software and lead analysis of the world's largest database of facial expression videos. His work in machine learning, AR and affective computing 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 and has spoken at TEDx Berlin and SXSW. We is a member of the ACM Future of Computing Academy.