Hand Gesture Recognition Based Speed and Direction Control of DC Motor Using CNN

Gesture recognition is a way for computers and other electronics to read and understand the movement of various body portions as commands. Using neural networks and hardware control to recognize gestures shows how easy it is to combine complex algorithms with managing physical devices. This paper talks about a new way to acknowledge hand motions and use them controlling the speed and direction of a DC motor. A webcam is used to take pictures of the hands, CNNs are used to process the images in MATLAB, and the motor is controlled by a microcontroller. The goal is to develop an easy and effective way to use hand movements to control motors. In real-time, the movements are analyzed and put into groups. The motor’s direction is then set by control commands from these movements. The system’s ability to quickly and accurately recognize gestures and control motors. When an external dataset is successfully validated, the accuracy that is created usually stays high, between 98.70% and 99.90%.