Vision-Based Hand Gesture Recognition as an Accessible Alternative to Conventional Game Input for Players with Disabilities

This paper presents a vision-based hand gesture recognition system designed as an accessible game input alternative for individuals with motor and speech impairments. This system utilizes computer vision techniques to detect and classify hand gestures, which are then translated into keyboard inputs using PyAutoGUI. By mapping predefined gestures to directional keys (← ↑ ↓ →), this system allows users to interact with games without relying on physical controllers or modifying game code. Usability testing was conducted using the System Usability Scale (SUS), yielding an average score of 65.62, indicating marginal acceptability and highlighting areas for refinement. The results demonstrate the potential of gesture-to-keyboard translation as a non-invasive, scalable, and low-cost approach to improve accessibility in digital environments, particularly gaming. Future work will focus on improving recognition accuracy and response time.