RESUMO

One of the biggest problems faced by amputees is obtaining a suitable low-cost prosthesis. To address this problem, the design and implementation of a transradial prosthesis controlled by electroencephalographic (EEG) signals was carried out. This prosthesis is an alternative to prostheses using electromyographic (EMG) signals, which are very complex and exhausting for the patient to execute. We collected EEG signal data using the Emotiv Insight Headset, which were then processed to control the movement of the prosthesis, known as the Zero Arm. Additionally, we incorporated Machine Learning algorithms to classify different types of objects and shapes. The prosthesis also features a haptic feedback system, which simulates the function of mechanoreceptors in the skin, providing the user with a sense of touch when using the prosthesis. Our research has yielded a viable and cost-effective prosthetic limb. We utilized 3D printing and easily obtainable servomotors and controllers, making the prosthesis affordable and accessible. Performance tests of the Zero Arm prosthesis have yielded promising results. The prosthesis demonstrated an average success rate of 86.67% across various tasks, indicating its reliability and effectiveness. Additionally, the prosthesis has an average recognition rate of 70% for different types of objects, a noteworthy accomplishment.