INTERACT: Inspection of Normal and Typical Encounters Requiring Asymmetric Collection and Tracking

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W911QX-12-C-0070
Agency Tracking Number: D121-002-0029
Amount: $99,771.00
Phase: Phase I
Program: SBIR
Awards Year: 2012
Solicitation Year: 2012
Solicitation Topic Code: SB121-002
Solicitation Number: 2012.1
Small Business Information
Aptima, Inc.
12 Gill Street, Suite 1400, Woburn, MA, -
DUNS: 967259946
HUBZone Owned: Y
Woman Owned: Y
Socially and Economically Disadvantaged: Y
Principal Investigator
 Alan Carlin
 Modeling and Simulations Scientist
 (781) 496-2444
 acarlin@aptima.com
Business Contact
 Thomas McKenna
Title: Chief Financial Officer
Phone: (781) 496-2443
Email: mckenna@aptima.com
Research Institution
N/A
Abstract
Because social interactions are ubiquitous for both the police and the military, it is crucial to improve their outcomes. However, assessing the success or failure of social interactions can be rather cumbersome. In order to capture and measure these interactions, video, sound, movement, and other forms of data must be collected and analyzed. However these methods require that comprehensive data is collected from all individuals involved. What is needed are ways to"fill in the gaps"when data are missing. In this proposal, titled INTERACT, we propose to develop these methods using supervised learning through support vector machines and temporal learning through a Hidden Markov Model (HMM) representation. Supervised learning allows the system to predict missing data based on patterns in the available data. The Hidden Markov Models will then assess and predict the interaction dynamics. Linking these methods in a feedback loop will allow each learning method to benefit from the conclusions of the other. This methodology will be verified by assessing and predicting interactions within existing data sets.

* information listed above is at the time of submission.

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