Segmenting and Labeling On-Body Sensor Data Streams with CRFs and Factor Graphs
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Ubiquitous mobile physiological sensing has the potential to profoundly improve our understanding of human health and behavior and to inform the design of more targeted treatments for a wide variety of conditions. However, extracting useful knowledge about high-level behaviors and activities from noisy low-level sensor data is an extremely challenging problem. In this talk, I will present two projects focused on segmenting and labeling on-body sensor data streams. The first project leverages dynamically generated conditional random field (CRF) models to parse wireless ECG data as a basic building block in a system designed to detect cocaine usage through its effect on ECG morphological structure. The second project is based on a novel and more general factor graph model for hierarchical segmentation and labeling that is being applied to a variety of domains including the detection and delineation of episodes of smoking and eating based on respiration and gestural data.
I am an assistant professor in the School of Computer Science at the University of Massachusetts Amherst where I co-direct the Machine Learning for Data Science Lab with Brendan O'connor and Dan Sheldon. I was previously a fellow of both the Pacific Institute for the Mathematical Sciences and the Killam Trusts at the University of British Columbia where I was based in the Laboratory for Computational Intelligence in the Department of Computer Science. I completed my PhD in machine learning in the Department of Computer Science at the University of Toronto.