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

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$99,771.00
Award Year:
2012
Program:
SBIR
Phase:
Phase I
Contract:
W911QX-12-C-0070
Award Id:
n/a
Agency Tracking Number:
D121-002-0029
Solicitation Year:
2012
Solicitation Topic Code:
SB121-002
Solicitation Number:
2012.1
Small Business Information
12 Gill Street, Suite 1400, Woburn, MA, -
Hubzone Owned:
Y
Minority Owned:
Y
Woman Owned:
Y
Duns:
967259946
Principal Investigator:
Alan Carlin
Modeling and Simulations Scientist
(781) 496-2444
acarlin@aptima.com
Business Contact:
Thomas McKenna
Chief Financial Officer
(781) 496-2443
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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