Designing training programs such that students are fully prepared for real-world scenarios can be challenging, particularly when preparing individuals for safety-critical roles that necessitate performing under conditions of high stress and mental workload. A possible avenue for improving training programs is to adapt them according to the stress and mental workload experienced by trainees during each training scenario. However, self-assessments can disrupt the training process and may not always be accurate. To address this, the project proposes a continuous neurophysiological monitoring system that tracks each trainee’s stress and mental workload, allowing instructors to view their development during training scenarios. The deployment of the system was piloted with four participants at the same time as they performed several conditions of the Revised Multi-Attribute Task Battery. Stress was induced by increasing the number of events, adding unpleasant sounds, and informing participants their tasks will be graded by the experimenters, while increased workload was induced by asking participants to perform more subtasks at the same time. After each task, participants provided subjective measures of stress and mental workload. Throughout the experiment, cardiorespiratory and brain activity were collected via a near-infrared spectroscopy headband, a smartshirt, and a smartwatch. These signals were processed in real time by the Sensor Hub system, running previously developed models of stress and mental workload for all four participants. The pilot data demonstrate the feasibility of instructors monitoring multiple students simultaneously using wearable sensors and real-time signal processing, with the potential to better prepare individuals for high stress and workload roles.