Next-Generation Tactical-Situation-Assessment Technology (TSAT): Iconic Language

Craig Martell, Naval Postgraduate School
Joshua Kroll, Harvard University

Abstract

We present the results of using an extension of the FORM gesture dataset to predict the mid-level phenomenon of phase. We compare the results of human phase predition with automated prediction using machine-learning techniques. Specifically, we present the results of hidden-Markov model experiments using an extended version of the FORM data to predict phase labels. Additionally, we compare FORM to the currently most accepted method of data gathering in this field--motion capture--by comparing the predictive accuracy of the physical gesture models produced by FORM and motion capture for phase labeling.