Toward data-to-decision sensing environments to assess human intent from responses to stimuli
Cartik R. Kothari, David J. Russomanno, Ronald B. Sartain, Ronald Frankel · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
Remote detection of harmful intent is necessary for eective and appropriate countermeasures and will reduce risks to life and property. Trained human observers and sensor systems typically use facial expressions, gaits, gestures, perspiration, and a number of other observable characteristics as possible indicators of harmful intent with mixed results. It is proposed that responses of human subjects to external stimuli can be used as additional indicators of harmful intent in surveillance contexts. A variety of alerting stimuli, possible responses to the stimuli, features to be sensed by sensors, and the utility of these sensed features as indicators of harmful intent are discussed in this paper. An ontology-based data-to-decision framework for assessing human intent, which would leverage the formal representations of the alerting stimuli, as well as the variety of possible responses, is proposed in the context of Semantic Web infrastructure.