College - Author 1
College of Engineering
Department - Author 1
Computer Science Department
Degree Name - Author 1
BS in Computer Science
Date
12-2025
Primary Advisor
April Grow, College of Engineering, Computer Science Department
Abstract/Summary
First-person shooter (FPS) games often demand high levels of skill in aiming, which leads players to look for external tools to improve their performance. This is where the concepts of aim training and aim trainers come in, becoming an easily accessible outside source for players to strengthen their performance with custom scenarios outside a set game. While many aim trainers exist, they offer limited insight into player performance metrics or adaptability to varying aiming styles. Furthermore, most existing aim trainers lack a standardized way of correlating aim skill with real-world performance or personalized feedback. This aim trainer addresses these limitations by providing a modular training environment that includes multiple aiming tasks with real-time feedback, a machine learning integration to adapt to player weaknesses, and precise scoring and accuracy systems. The goal in this project and the aim trainer is to both train reflexes and tailor the training experience to each player, adjusting their sensitivity in real time to follow their performance and help them find an optimal number to play with.
URL: https://digitalcommons.calpoly.edu/cscsp/182