Development of a Low-Cost Prosthetic Hand Using Electromyography and Machine Learning
This project developed a low-cost myoelectric prosthetic hand for amputees in developing countries. It used EMG signals from three muscles to classify five different gestures using a shallow neural network, achieving high accuracy (up to 98.6%). The hand controls two degrees of freedom via servo motors and includes a wrist rotation mechanism and tactile feedback for enhanced functionality.