Electromyography Controlled Smart Prosthetic Arm

Prosthetic technology has advanced to the point where intelligent prosthetic hands have been designed in an effort to restore functional capacities and improve quality of life for those who have undergone upper limb amputations. A novel electromyography (EMG)-based smart prosthetic hand is being built in this effort using deep learning techniques. Using non-invasive surface electromyography (EMG) data— the electrical impulses generated by muscles during contractions—the suggested smart prosthetic hand decodes the user's intended motions. To interpret and understand the complex EMG data associated with different hand motions, deep learning techniques called convolutional neural networks (CNNs) are employed. Deep learning made it possible through instruction allows for natural and real-time movement of the prosthetic hand. Smooth and instantaneous control over the prosthetic hand is made possible by the taught deep learning model, which accurately maps EMG signals to particular hand movements. Lastly, this demonstrates a deep learning algorithm-driven EMG-based smart prosthetic hand that may be able to enable people who have lost upper limb function regain dexterity when using prosthetic limbs.