Human-Centered Intelligent Training for Emergency Responders

Authors

  • Ranjana K. Mehta Texas A&M University
  • Jason Moats Texas A&M Engineering Extension Service
  • Rohith Karthikeyan Texas A&M University
  • Joseph L. Gabbard Virginia Tech
  • Divya Srinivasan Virginia Tech
  • Eric Jing Du University of Florida
  • Alexander Leonessa Virginia Tech
  • Garret Burks Virginia Tech
  • Andrew Stephenson Knowledge Based Systems, Inc.
  • Ron Fernandes Knowledge Based Systems, Inc.

DOI:

https://doi.org/10.1609/aimag.v43i1.19129

Abstract

Emergency response (ER) workers perform extremely demanding physical and cognitive tasks that can result in serious injuries and loss of life. Human augmentation technologies have the potential to enhance physical and cognitive work-capacities, thereby dramatically transforming the landscape of ER work, reducing injury risk, improving ER, as well as helping attract and retain skilled ER workers. This opportunity has been significantly hindered by the lack of high-quality training for ER workers that effectively integrates innovative and intelligent augmentation solutions. Hence, new ER learning environments are needed that are adaptive, affordable, accessible, and continually available for reskilling the ER workforce as technological capabilities continue to improve. This article presents the research considerations in the design and integration of use-inspired exoskeletons and augmented reality technologies in ER processes and the identification of unique cognitive and motor learning needs of each of these technologies in context-independent and ER-relevant scenarios. We propose a human-centered artificial intelligence (AI) enabled training framework for these technologies in ER. Finally, how these human-centered training requirements for nascent technologies are integrated in an intelligent tutoring system that delivers across tiered access levels, covering the range of virtual, to mixed, to physical reality environments, is discussed.

Downloads

Published

2022-03-31

How to Cite

Mehta, R. ., Moats, J. ., Karthikeyan , R. ., Gabbard, J. ., Srinivasan, D. ., Du, E. ., Leonessa , A. ., Burks, G. ., Stephenson, A. ., & Fernandes, R. . (2022). Human-Centered Intelligent Training for Emergency Responders. AI Magazine, 43(1), 83-92. https://doi.org/10.1609/aimag.v43i1.19129

Issue

Section

Special Topic Articles