Investigation of Different Approaches to Real-Time Control of Prosthetic Hands With Electromyography Signals
João Olegário de Oliveira de Souza, Marcos Daniel Bloedow, Felipe Cezimbra Rubo, Rodrigo Marques de Figueiredo, Gustavo Pessin, Sandro José Rigo
Abstract
In this article, we describe a real-time system for prosthetic hands control. The system architecture includes the integration of the electromyographic (EMG) signal acquisition devices, platform for the implementation of the real-time classifier, sensors for the detection of object slip after grasp and the open-source hand prosthesis. Three databases were used to evaluate the implemented classifiers: a database with EMG data from local volunteers and NinaPro DB2 and DB3 databases that include electromyography and accelerometry (ACC) data acquisitions. A Multilayer Perceptron (MLP) classifier was implemented on a platform for rapid prototyping (Raspberry Pi 3 model B+) and generated responses in real-time (11 ms) with average accuracy of 96.30% for 11 hand and wrist gestures/movements.