Augmented Cognition in Human-System Interaction through Coupled Action of Body Sensor Network and Agent Based Modeling

This research proposes a conceptual model to make significant progress in augmented cognition. We intend to design a generic architecture to improve the collective situational awareness (SA) of a team of soldiers on the battlefield during combat effects, and cultural communications. It also establishes a training outline for future landscapes of a similar nature by providing convincing feedback. It will serve as architecture of a model, based on the current laboratory framework. This involves data analysis of the feedback from the neurophysiological body sensors (heart rate, skin temperature, breathing rate and others) placed on a wearable immersive training vest. It includes gesture tracking gloves and fixed spatial locators. Approximately 15 soldiers will be incorporated into the study. Various training scenarios will be designed to capture all possible body perturbations, movements, and interactions. A similar agent-based model (ABM) of the training environment will be created to augment the. soldier's cognitive competencies. ABM will integrate social systems interaction complexity into the decision making capability of the soldier in real time. Automated feedback can be provided using the model developed in the event of any social faux pas via gesture tracking and monitoring cognitive capacity. Model can help discover novel talents and render knowledge during training scenarios. (C) 2013 The Authors. Published by Elsevier B. V. Selection and/or peer-review under responsibility of Georgia Institute of Technology.