| dc.description.abstract | The technological advances in the last years have allowed the development of hand prostheses that have more movement precision, weight reduction and the use of bioelectric signals in its operation. Nowadays, the prostheses with myoelectric control are considered the state of art in this segment; they represent a great tool in the restoration of parts of daily tasks and in the improvement of life quality for upper limb amputees. However, the control of these devices is not intuitive, because the users of myoelectric prostheses need to perform complex sequences of muscle contraction impulses to change the type of movement. The goal of this thesis was the development of a real-time myoelectric control of a hand prosthesis using Machine Learning. The system architecture includes the integration of the electromyographic (EMG) signal acquisition devices, platform for the implementation of the real-time classifier and interface for servomotor driver for an open source hand prosthesis. The following classifier models were implemented and compared: Multilayer Neural Network, Convolutional Neural Network, Recurrent Neural Network using LSTM units and Random Forest. Firstly, the assays were performed on offline systems involving the three databases processing, incrementally incorporating and evaluating different resources and sensors until the implementation of the online system. A Multilayer Perceptron (MLP) classifier was implemented on a platform for rapid prototyping (Raspberry Pi 3 model B+) obtaining average accuracies of 96.3% (offline) and 87.2% (online) and responses in real-time (10.3 ms) for 11 hand gestures. | en |
