Development of a Fast-Running Algorithm to Approximate Incident Blast Parameters Using Body-Mounted Sensor...

Area of Science:

• Biomedical Engineering
• Military Medicine
• Signal Processing

Background:

• Developing standardized metrics for blast overpressure exposure is crucial for military personnel safety.
• Interpreting data from body-worn blast sensors presents challenges in combat and training scenarios.
• Accurate accumulation of blast exposure profiles requires reliable, comparable data.

Purpose of the Study:

• To develop a rapid, in-field solution for processing blast sensor data.
• To create an automated algorithm for approximating incident blast parameters.
• To enable science-based stand-down decisions for service members exposed to blast overpressure.

Main Methods:

• Developed the Fast Automated Signal Transformation (FAST) algorithm to process pressure-time data from blast sensors.
• Utilized incident pressure as the standardized output metric for physiological relevance and directional independence.
• Preprocessed data and automatically flagged non-blast events (false positives).

Main Results:

• FAST algorithm results showed good agreement with experimental data and high-fidelity numerical simulations.
• Evaluated FAST performance for body shielding, irregular charges, and detonations within structures.
• FAST predictions accurately accounted for shock reflections, with pressure estimates typically within 20% of anticipated values.

Conclusions:

• The FAST algorithms provide a standardized method for analyzing body-mounted blast sensor data.
• FAST accounts for shock interactions, enabling direct comparison of individual blast exposures.
• Future development aims to include heavy weapons data for comprehensive blast exposure profiling.