Neural, Muscular, and Perceptual responses with shoulder exoskeleton use over Days

Passive shoulder exoskeletons have been widely introduced in the industry to aid upper extremity movements during repetitive overhead work. As an ergonomic intervention, it is important to understand how users adapt to these devices over time and if these induce external stress while working. The study evaluated the use of an exoskeleton over a period of 3 days by assessing the neural, physiological, and perceptual responses of twenty-four participants by comparing a physical task against the same task with an additional cognitive workload. Over days adaptation to task irrespective of task and group were identified. Electromyography (EMG) analysis of shoulder and back muscles reveals lower muscle activity in the exoskeleton group irrespective of task. Functional connectivity analysis using functional near infrared spectroscopy (fNIRS) reveals that exoskeletons benefit users by reducing task demands in the motor planning and execution regions. Sex-based differences were also identified in these neuromuscular assessments.Comment: Poster Abstract, Submitted to Neuroergonomics Conference and NYC Neuromodulation Conferences, July 28 to 31, 202

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

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