Wearable Tracking of Eye and Body Movements During Breaching Training: Toward Real-Time Blast Injury Monitoring

Area of Science:

• Neuroscience
• Biomedical Engineering
• Occupational Health

Background:

• Occupational blast overpressure exposure is linked to cognitive, psychological, and neurosensory deficits.
• Monitoring physiological responses to blast exposure is crucial for understanding and mitigating risks.

Purpose of the Study:

• To develop and validate a wearable system for simultaneous monitoring of physiology and blast exposure.
• To establish a machine learning-based dose-response model correlating physiological changes with blast exposure levels.

Main Methods:

• A wearable system integrated electrooculography, gait, and balance sensors.
• Machine learning (Fused model) was employed to create a risk score predicting blast exposure levels.
• The model fused multiple physiological measures to predict blast exposure in subjects.

Main Results:

• The Fused model achieved a correlation coefficient (R) of 0.60 in predicting blast exposure.
• Physiological changes were observed in relation to blast events with peak pressure levels as low as 0.3 pound per square inch (PSI).
• An individual subject exhibited increasing reaction times and anomalous physiological risk scores after low-level blast exposures.

Conclusions:

• Wearable technology is a viable tool for measuring physiological changes related to occupational blast exposure.
• This approach may aid in preventing neurotrauma from repeated exposures.
• Personalized and population-based safety limits for blast exposure could be informed by this methodology.