Predicting rifle shooting accuracy from context and sensor data : A study of how to perform data mining and knowledge discovery in the target shooting domain

Abstract: The purpose of this thesis is to develop an interpretable model that gives predictions for what factors impacted a shooter’s results. Experiment is our chosen research method. Our three independent variables are weapon movement, trigger pull force and heart rate. Our dependent variable is shooting accuracy. A random forest regression model is trained with the experiment data to produce predictions of shooting accuracy and to show correlation between independent and dependent variables. Our method shows that an increase in weapon movement, trigger pull force and heart rate decrease the predicted accuracy score. Weapon movement impacted shooting results the most with 53.61%, while trigger pull force and heart rateimpacted shooting results 22.20% and 24.18% respectively. We have also shown that LIME can be a viable method to give explanations on how the measured factors impacted shooting results. The results from this thesis lay the groundwork for better training tools for target shooting using explainable prediction models with sensors.