Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

Brain Sciences

Jing Du¹  Maher Abujelala²  Ranjana K. Mehta²  Oshin Tyagi²  Rohith Karthikeyan³ 

[1] Department of Civil and Coastal Engineering, Engineering School of Sustainable Infrastructure and Environment (ESSIE), Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA;Department of Industrial & Systems Engineering, Texas A & M University, College Station, TX 77843, USA;Department of Mechanical Engineering, Texas A & M University, College Station, TX 77843, USA;

关键词: VR;    fNIRS;    machine learning;    firefighters;    emergency responders;    learning;   

DOI  :  10.3390/brainsci11070885

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown