Repeated exposure to blast overpressure in occupational settings has been associated with changes in cognitive and psychological health, as well as deficits in neurosensory subsystems. In this work, we describe a wearable system to simultaneously monitor physiology and blast exposure levels and demonstrate how this system can identify individualized exposure levels corresponding to acute physiological response to blast exposure. Machine learning was used to develop a dose-response model that fused multiple physiological measures (electrooculuography, gait, and balance) into a single risk score by predicting the level of blast exposure on held-out subjects (Fused model, R = 0.60). For our system, cohort, and environment, we found that blast events with peak pressure levels as low as 0.3 pound per square inch (PSI) could be related to physiological changes with blast exposure. We also identified an individual subject with longitudinally increasing reaction time scores who consistently showed a rapid and anomalous change in physiology-based risk scores after exposure to low-level blast events. Our results suggest that wearable technology may be viable for measuring physiological changes that are related to occupational blast exposure. Ultimately, this approach might be used to prevent neurotrauma from repeated exposure and to help set limits for a population or on an individual basis.
Kemmerer et al. (Wed,) studied this question.