Search for: All records

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. 

The nature of firefighters’ duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.