Presented at BIOSTEC 2020, 13th International Joint Conference on Biomedical Engineering Systems and Technologies, Valletta, Malta (February 2020)
Medical personnel and first responders are often deployed to dangerous environments where their success at saving lives depends on their ability to act quickly and effectively. During training, non-invasive measurement of cognitive performance can provide trainers with insight into medical students’ skill mastery. Functional Near-Infrared Spectroscopy (fNIRS) is a direct and quantitative method to measure ongoing changes in brain blood oxygenation (HbO) in response to a person’s evolving cognitive state (i.e., cognitive workload or mental effort) that has only recently received significant attention for use in the real world. The work presented here includes data collection with a new, more portable, rugged design of an fNIRS sensor to test the functionality of this new sensor design and our ability to measure cognitive workload in a medical simulation training environment. To assess sensor and model accuracy, during breaks from the training, participants completed a gold-standard, laboratory task and during training in a medical simulation environment. Linear mixed model ANOVA showed that when we accounted for fixed effects of intercept and slope in our model, there was a significant difference in the HbR Ch1 model for n-back load (coef=0.009, p=0.034), intercept (coef=0.96, p=1.21e-07***), and load (slope) (coef=-0.09, p=0.03). Future work will present results of the data collected during the disaster response medical simulation training.
1 Charles River Analytics
2 University of Massachusetts Medical School
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