DEVELOPMENT OF A SMART PROSTHETIC ARM CONTROLLED BY EMG SENSORS AND AI

The increasing prevalence of limb loss, whether due to congenital disorders or traumatic events, has accelerated the demand for innovative and efficient prosthetic technologies. In response to this, the paper proposes an advanced prosthetic arm control system that integrates electromyography (EMG) sensors and artificial intelligence (AI) for seamless, user-driven motion. The system leverages the capability of EMG sensors to detect electrical signals from remaining muscle tissue, which are then transmitted to a Raspberry Pi Pico microcontroller for real-time processing. These signals are interpreted by a machine learning model, specifically TensorFlow Lite, to control the movement of the prosthetic arm's servo motors with high precision. This setup facilitates intuitive control, allowing users to execute complex gestures and actions with ease. The inclusion of AI not only increases the accuracy of motion predictions but also enables the system to adapt to the unique muscle signals of individual users, offering a more personalized experience. The results highlight the potential of combining bioelectric signal detection with machine learning to revolutionize prosthetic functionality, ultimately enhancing the autonomy and quality of life for users facing limb loss.