Keywords: ADAPTIVE;AFTER ACTION REVIEW (AAR);BEHAVIOR MODELING;BEST PRACTICES;COGNITIVE;ENHANCING PERFORMANCE;HUMAN PERFORMANCE;PERFORMANCE;PHYSIOLOGICAL ;PHYSIOLOGY;WEARABLE DEVICES
Learning Objective 1: Learners will remember and understand how physiological measurements can be beneficial to training and simulation environments by providing at least three example use cases.
Learning Objective 2: Learners will be able to apply the eight recommended steps of integrating physiological sensing.
Learning Objective 3: Learners will be able to evaluate physiological data test plans in accordance with best practices.
Abstract:
Hyper-realistic environments and on-demand training tools have experienced significant advancements in training and simulation use cases. Incorporating physiological monitoring into simulation and training environments provides crucial information to monitor and optimize performance, ensure individual competencies, provide adaptive support, and enable bi-directional communication between human users and AI collaborators. Training and simulation communities can remain at the forefront of innovation and assured deterrence by synergizing common needs and removing barriers to integrating human performance monitoring. The key to making these capabilities available to the community is streamlining an approach that is adaptive to a variety of use cases. Such an approach will support more advanced training environments, decision-making, and digital engineering to ensure readiness.
Attendees will be equipped with tools to understand and implement physiological monitoring, regardless of specific use cases. The session will provide engaging overviews of the current state-of-the-art in physiological monitoring and human performance, including use cases for training and simulation, current challenges, and example implementations. Attendees will learn best practices and a recommended approach to leverage physiological sensing in various environments. The approach will delve into understanding underlying physiological changes, selecting appropriate sensors, benchmarking to confirm accuracy, analyzing data, storing results, and translating data into action.
