Award Abstract #0845683
CAREER: Enabling Community-Scale Modeling of Human Behavior and its Application to Healthcare

NSF Org:	IIS Division of Information & Intelligent Systems
Initial Amendment Date:	March 3, 2009
Latest Amendment Date:	March 3, 2009
Award Number:	0845683
Award Instrument:	Continuing grant
Program Manager:	Edwina L. Rissland IIS Division of Information & Intelligent Systems CSE Directorate for Computer & Information Science & Engineering
Start Date:	March 1, 2009
Expires:	November 30, 2011 (Estimated)
Awarded Amount to Date:	$290,445.00
Investigator(s):	Tanzeem Choudhury tanzeemc@gmail.com (Principal Investigator)
Sponsor:	Dartmouth College OFFICE OF SPONSORED PROJECTS HANOVER, NH 03755-1404 (603)646-3007
NSF Program(s):	ROBUST INTELLIGENCE
Program Reference Code(s):	HPCC, 9102, 9150, 9215, 1045, 1187
Program Element Code(s):	7495

ABSTRACT

Research supported by this award is developing community-based methods for sensing, recognizing, and interpreting human activities from body-worn sensors. Specifically, this research is

1) developing systems that learn new classes of activity with minimal human supervision, where the system queries a human user for additional information on an activity being learned, but only when such queries are informationally necessary and behaviorally unobtrusive,

2) developing the paradigm of community-guided learning, which leverages people's social ties and behavioral similarities, in order to define an efficient scheme for sharing various aspects of the underlying activity classes across many individuals, and

3) evaluating the new community-guided learning methods by using them to learn about (a) social isolation and functional independence among elderly persons, and (b) social interaction among high-functioning autistic children.

Speaking generally, the research is advancing machine learning and artificial intelligence, especially in the areas of semi-supervised, active, and relational learning. Beyond these basic scientific contributions, the resulting research has the potential to transform community health assessment by collecting fine-grained clinically-relevant information continuously, cheaply, and unobtrusively, over long periods of time. This research also opens up many opportunities for education and outreach, in part because it is pushing machine learning and artificial intelligence into social and societally-important realms, promising to attract groups, notably women, who are under-represented in computer science.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Danny Wyatt, Tanzeem Choudhury, James Kitts, and Jeff Bilmes.. "Inferring Colocation and Conversation Networks from Privacy-senstive Audio with Implications for Computational Social Science.," ACM Transactions on Intelligent Systems and Technology, v.1, 2011.

BOOKS/ONE TIME PROCEEDING

Danny Wyatt, Tanzeem Choudhury, and Jeff Bilmes. "Discovering Long Range Prop