Quantitative team performance metrics for dismounted infantry battle drill analysis

Area of Science:

• Military science
• Human performance optimization
• Wearable technology

Background:

• Effective military operations depend on optimizing small unit performance.
• Current battle drill training lacks objective, quantitative metrics for evaluating team performance.
• Need for advanced assessment tools to supplement traditional observer-controller feedback.

Purpose of the Study:

• To identify wearable-derived measures that predict military squad performance during battle drills.
• To develop quantitative models for assessing key performance constructs.
• To explore the integration of wearable data into future after-action review systems.

Main Methods:

• Utilized data from wearable sensors including inertial measurement units (IMU) and Global Positioning System (GPS).
• Incorporated physiological data (heart rate) and transcribed communication metrics.
• Developed linear mixed-effects models to analyze performance data against specific battle drill constructs.

Main Results:

• Wearable-derived measures significantly predicted squad performance across three critical constructs: Communications, Fire Effectiveness, and Violence of Action.
• Models explained a substantial variance in squad performance, ranging from 51.5% to 63.5%.
• Identified specific sensor-based metrics as reliable indicators of team effectiveness.

Conclusions:

• Wearable technology offers a viable solution for objective, quantitative assessment of military team performance.
• Integrating these metrics into after-action reviews can provide deeper insights beyond traditional feedback.
• This approach has the potential to significantly enhance military training and operational readiness.