NeuroTune is an AI-driven aim trainer that adapts in real-time to improve your FPS skills by dynamically adjusting the size, spawn location, and lifetime of targets based on your performance.
As competitive gaming and eSports gain momentum with titles like Counter-Strike, Valorant, Call of Duty etc., we recognized a gap in the market for personalized training tools. Traditional aim trainers like AimLabs and Kovaak's offer great scenarios, but they lack real-time adaptability and personalization. NeuroTune was conceived to bridge this gap by leveraging cutting-edge AI to offer a training experience that adjusts in real-time, offering unprecedented personalization that hasn't yet been seen in commercially available trainers.
Click the image to watch the NeuroTune AI Aim Trainer demo!
In-Game screenshot of NeuroTune's dynamic target adaptation in action.
- Adaptive Training: Real-time adjustments of target size, spawn location, and lifetime based on player performance.
- Fitts' Law Integration: Uses Fitts' Law to create a training environment that adjusts difficulty dynamically.
- Customizable Scenarios: to add @mark
- Performance Tracking: Tracks hits, misses, and reaction times to optimize future training sessions.
- App/Trainer: Built using Unity for 3D environments and Python for the AI-driven components.
- AI Integration: Machine learning algorithms analyze player performance to adjust difficulty in real-time.
- Fitts' Law: Implemented as the core mechanic to control target behavior based on distance, size, and player response time.
We faced difficulties integrating Fitts' Law into a real-time system, as calculating dynamic target parameters while ensuring smooth gameplay required careful balancing. Additionally, creating an adaptive system that doesn't frustrate users while remaining challenging proved to be a balancing act.
We successfully implemented real-time adaptability that provides meaningful and tailored training sessions. We also received positive feedback from initial testers, who saw significant improvement in their aiming accuracy after just a week of using NeuroTune.
We learned the value of combining machine learning with game mechanics to create more personalized experiences. Additionally, we gained deeper insights into how principles like Fitts' Law can be applied to enhance user interaction in a game environment.
NeuroTune serves as a proof of concept to demonstrate the potential of AI-driven adaptability in aim training applications. As of August 2024, this project showcases an innovative approach that has not yet been implemented in any commercial aim trainer on the market. While we plan to refine the machine learning algorithms to provide even more precise adaptability based on user performance, the current implementation serves primarily to validate the concept.
During our final testing phase, one of our playtesters experienced such a drastic improvement in their aim that they managed to hit a record Kills Per Minute (KPM) in Counter-Strike 2 after just one week of training with NeuroTune!
 Hans ONG
 @thehansong
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Mark LOW
@Markkeroni
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