Flex Sensor and Machine Learning-Based Mute Communication System

Individuals facing speech impairments encounter significant communication challenges. To address this, we have developed a groundbreaking system that seamlessly translates custom hand gestures into written text and spoken language. Our combines flex sensors state-of-the-art machine learning techniques, empowering individuals with to express themselves effectively improve their overall experience. By connecting an Arduino board utilizing MATLAB for robust data collection, analysis, processing, achieve highly accurate gesture recognition system. Through meticulous steps of labeling, rigorous training model, thorough accuracy evaluations, our recognizes wide range gestures, including numbers 0-9, letters A-Z, 10 Kannada language conveying meaningful phrases. The achieved notable performance 83.5%, precision 88.6%, recall 95.1%, F1 Score 91.7%. Key Words: impairments, recognition, sensors,