1. Proceedings
  2. I3M
  3. 2018
  4. DHSS
  5. 10.46354/i3m.2018.dhss.006

Machine learning for approximated sensors for Marksmanship training

  • a Keith Brawner  
  • aUnited States Army Research Laboratory, Human Research and Engineering Directorate
Cite as
Brawner K. (2018). Machine learning for approximated sensors for Marksmanship training. Proceedings of the 8th International Defence and Homeland Security Simulation Workshop (DHSS 2018), pp. 39-45. DOI: https://doi.org/10.46354/i3m.2018.dhss.006
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Abstract

All people in the military must be proficient on the basics – to shoot, move, and communicate. Basic Rifle Marksmanship (BRM) is required for both noncommissioned and enlisted Warfighters in all branches of military service, with training on BRM skills being conducted in a series of “dry fire”, simulation, and live drills. In all phases of training, Warfighters receive instruction on the four fundamentals of shooting: breathing, body position, sight picture, and trigger squeeze. Within simulation, this training is conducted in a 1:4 to 1:8 range; one instructor per 4-8 students. While Realtime individualized feedback is a goal of instruction, it is not a reality, as instructors must attend to the needs of many students. In an effort to aid instructors in providing valuable individualized feedback, a tutoring system was developed which automatically diagnosed novice performance when compared to experts. This system was used to diagnose novice performance with extensive physical sensors applied to the weapons. This work investigates whether machine learning can aid in the diagnosis of the novice performance, without the physical sensors, and analyzes the degree to which the sensors are necessary.

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