AI-based Intelligent System for Biomechanical Grip Analysis and Personalized Pistol Handle Design in Sport Shooting

Paper ID : 1712-SPORTCONGRESS

Authors

امیر عباسقلی پور *, مهربان پای سروی حسینی دانشگاه تهران

Abstract

Introduction: This study presents the design of an intelligent, data-driven system for biomechanical grip analysis and personalized pistol handle optimization in sport shooting. Conventional grip fitting methods rely on trial and error and subjective feedback. The proposed system introduces a scientific and reproducible approach based on real-time pressure data and artificial intelligence (AI) analysis to enhance precision, stability, and control during shooting. Methods: The system uses a bio-moldable thermoplastic material to capture the anatomical shape of the shooter’s hand. Embedded force-sensing resistors (FSRs) measure finger and palm pressure distribution during grip execution. Data are transmitted to an ESP32 microcontroller for analysis using AI algorithms to identify optimal pressure zones and detect imbalance patterns. The software interface provides real-time visual feedback and corrective recommendations. The system can be applied in training, rehabilitation, or ergonomic evaluation. Results: Preliminary testing demonstrated the ability of the system to accurately capture grip pressure distribution and suggest corrective adjustments through adaptive AI feedback. The proposed framework allows shooters to improve grip consistency, minimize hand strain, and achieve better shot precision. The system supports repeatable data-driven analysis and customization without altering the official dimensions of the standard pistol grip. Conclusion: This innovative AI-based system provides a novel approach for biomechanical assessment and personalized training in sport shooting. It can serve as an analytical and corrective tool for athletes and coaches aiming to improve performance and prevent overuse injuries.

Keywords

Artificial Intelligence, Sport Shooting, Grip Pressure, Biomechanics, Training Optimization

Status: Abstract Accepted (Oral Presentation)