Mathematics Events
[PAST EVENT] Human Sensing Using Light
Speaker: Tianxing Li, Dartmouth College
Abstract:
Long-term, continuous monitoring of human behaviors and biological markers provides essential input for data-driven health technologies. Existing sensing systems, however, still have significant drawbacks. Wearable and wellness sensors (e.g., Apple Watch, Fitbit) require frequent charging and impose extra burdens on users, while camera-equipped systems present serious privacy risks by capturing raw images. Tackling these challenges demands novel system designs that can strike a better balance between sensing granularity, power consumption, and privacy.
In this talk, I will describe our recent efforts to enable a variety of sensing capabilities with minimal, low-level sensing data. Specifically, we have explored the use of light (visible or near infrared light) as the sensing medium and studied how the human body interacts with light to infer fine-grained behaviors and biomarkers. I will first present my work on reconstructing skeleton poses. Existing methods all require invasive cameras with a limited field of views. My work replaces cameras with photodiodes to capture how the user body blocks light and aggregates such binary blockage information observed in different directions to reconstruct skeleton poses. I will then describe our development of the first battery-free eye tracker without the need of cameras. It relies on a circular array of photodiodes around the eye to sense light reflected by the eye and infers pupil position and size. The system consumes only a few hundreds of microwatts of power and thus can be powered by the energy harvested from ambient light. Finally, I will conclude by discussing short-term and long-term research plans.
Bio:
Tianxing Li is a PhD candidate in Computer Science at Dartmouth College. He earned M.S. at Dartmouth College in 2014 and B.E. with honor from Australian National University in 2012. His research interests are in wireless communication, sensing, and low-power systems. His work on light-based sensing won MobiCom Best Video Award in 2015, and was selected as a Best Paper Nominee at SenSys 2017, SIGMOBILE Research Highlights in 2016 and 2018, and CACM Research Highlights in 2018. He has also received MobiSys Best Demo Award in 2015, VLCS Best Paper Award in 2014, and UbiComp Best Paper Nominee Award in 2014.
Contact
Zhenming Liu