Prosthetic hand for phocomelia patients with enhanced pain management using EMG signal processing and biofeedback control system

Abstract

Denoising is an essential step in data mining. It makes an effort to remove noise from the picture without sacrificing any of the important elements. The primary method utilized in this project to assess the effectiveness of phocomelia images is Kmeans clustering. The PSNR and MSE values of the denoised image are computed. In the end, the best technique for denoising medical images is chosen based on the PSNR values from the collection of patient data necessary for the fitting of upper limb devices. Using image processing, the bone health of phocomelia patients are evaluated and specific information extracted from MRI scans. Electromyography (EMG) sensors pick up electrical impulses, convert them into hand movements that the user wants, and flex the muscles in the residual limb directly below the elbow. The same muscles that enable hand function in humans are also felt by the system using microcontrollers. Through the integration of sophisticated sensor technologies with responsive prosthetic design idea, this newly developed technique enhances feedback from the proprioceptive system for patients with phocomelia, hence promoting natural movement and improving functional outcomes. As a result, it provides phocomelia patients a special method for acquiring superior result from biofeedback system.