Biologically inspired algorithms applied to prosthetic control

Biologically inspired algorithms were used in this work to approach different components of pattern recognition applied to the control of robotic prosthetics. In order to contribute with a different training paradigm, Evolutionary (EA) and Particle Swarm Optimization (PSO) algorithms were used to train an Artificial Neural Network (ANN). Since the optimal input set of signal features is yet unknown, a Genetic Algorithm (GA) was used to approach this problem. The training length and rate of convergence were considered in the search of an optimal set of signal features, as well as for the optimal time window length. The ANN proved to be an accurate pattern recognition algorithm predicting 10 movements with over 95% accuracy. Moreover, new combinations of signal features with higher convergence rates than the commonly found in the literature were discovered by the GA. It was also found that the PSO had better performance that the EA as a training algorithm but worse than the well established Back-propagation. The latter considered accuracy, training length and convergence. Finally, the common practice of using 200 ms time window was found to be sufficient for producing acceptable accuracies while remaining short enough for a real-time control