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Intelligent robotic prostheses employ pattern recognition techniques in their construction and, for this, adopt several approaches of Artificial Intelligence (AI). The study created a system called BioPatRec-Py (inspired by BioPatRec) that implements the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in a parallel hardware, using a lightweight architecture. The introduced system employed a set of strategies to make the classification process homogeneous, reduce training time and variability. The methodology fed the algorithm with features instead of the raw signal, providing the network with information that describes the movement (level of muscle activation, magnitude, amplitude, power, among others). The research utilized an adaptive Kaufman filter to remove noise from the series of features and adopted a quantile normalization system to make the distribution uniform and facilitate the training process. It was possible to train a generic network capable of operating in the entire population analyzed. Collective training is the main contribution of the research, as it allows the prosthesis to function on various individuals and potentially under different conditions. The individually evaluated networks reached 97.44% average accuracy with 0.69 seconds of training. The global model achieved an accuracy of 97.83% with a training time of 4.01 seconds.

Pattern Recognition in Myoelectric Signals Using Deep Learning, Features Engineering, and a Graphics Processing Unit