This paper outlines a proposal regarding the use of machine learning, specifically a long-short term model, to increase the military’s effectiveness and safety protocols. The approach is to collect data from weapons training and apply it to a model that can distinguish between weapon activities. By training the model on a dataset that consists of several common weapons activities, we hope to improve commanders' understanding of their troop's performance and readiness. The evaluation will consist of examining the loss of the model, its accuracy, and analyzing activities it frequently confused. This work will extend the current research in soldier activity recognition by introducing weapon activity recognition.