Towards continuous sensing of health and behavior
Speaker: Deepak Ganesan, U Mass at Amherst
Date/Time: 4pm, Nov 10, Mann Student Center
Towards continuous sensing of health and behavior
Deepak Ganesan
Associate Professor
Department of Computer Science, MASS Amherst
Abstract:
Wearable sensors offer tremendous opportunities for accelerating biomedical discovery, and improving population-scale health and wellness. There is a growing appetite for health analytics — we are no longer content with wearables that count steps and calories, we want to measure physiology, behavior, activities, cognition, and affect, with the expectation that such data will lead to deep insights that can improve quality of life.
But a chasm separates expectations and reality. How do we extract such insights from sensor platforms with tiny energy budgets? How do we communicate high-rate sensor data to the cloud for enabling deep analytics while operating within these energy budgets? How do we deal with noise, confounders, and artifacts that make insights hard to extract from signals collected in real-world settings?
In this talk, I will discuss a few strategies to tackle these problems in the context of driving applications. I will discuss how we can design an low-power computational eyeglass that continually tracks eye and visual context by leveraging sparsity, how we can transfer data at Megabits/second from microphones while operating at tens of micro-watts of power, and how we can infer smoking and drug use by leveraging machine learning tools like graphical models.
Bio:
Deepak Ganesan is Associate Professor in the Department of Computer Science at UMASS Amherst. He received his Ph.D. in Computer Science from UCLA in 2004 and his bachelors in Computer Science from IIT, Madras in 1998. His interests are in all aspects of wireless sensing, including ultra-low power wireless, sensor and mobile systems design and implementation, data analytics, and applications. He received the NSF CAREER Award in 2006 and the IBM Faculty Award in 2008. At UMass, he was selected as a Junior Faculty Fellow in 2008, and a Lilly Teaching Fellow in 2009. His recent work has been recognized by a Best Paper Award Runner-up at Mobicom 2014, a Best Paper Award at CHI 2013, and two honorable mentions at Ubicomp 2013.