SRI Authors: Rakesh “Teddy” Kumar
S. Khan, H. Cheng, R. Kumar, “A Hierarchical Behavior Analysis Approach for Automated Trainee Performance Evaluation in Training Ranges”, Foundations of Augmented Cognition, Lecture Notes in Computer Science, Volume 8027, 2013, pp 60-69
In this paper we present a closed loop mixed reality training system that provides automatic assessment of trainee performance during kinetic military exercises. At the core of our system is a hierarchical behavior analysis approach that integrates a number of data sensor modalities including Audio/Video, RFID and IMUs to automatically capture trainee actions in a comprehensive manner. Our behavior analysis and performance evaluation framework uses a finite state machine (FSM) model in which trainee behaviors are the states of the training scenario and the transitions of states are caused by stimuli that we refer to as trigger events. The goal of behavior analysis is to estimate the states of the trainees with respect to the training scenario and quantify trainee performance. To robustly detect each state, we build classifiers for each behavioral state and trigger event. At a given time, based on the state estimation, a set of related classifiers are activated for detecting trigger events and states that can be transitioned to and from the current states. The overall structure of the FSM and trigger events is determined by a Training Ontology that is specific to the training scenario.