Biologically inspired algorithms applied to prosthetic control
Paper i proceeding, 2012

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.

Biologically inspired algorithms

Particle swarm optimization algorithm

Time windows

Rate of convergence

Optimal sets

Convergence rates


Pattern recognition

Neural networks


Prosthetic controls


Signal features

Approximation theory

Genetic algorithms

Training algorithms

Real time control

Training length

Particle swarm optimization (PSO)

Input set

Pattern recognition algorithms

Rehabilitation engineering

Biomedical engineering

Optimal time

Biomedical signal processing


Signal processing


Max Jair Ortiz Catalan

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Rickard Brånemark

Göteborgs universitet

Bo Håkansson

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

BioMed 2012 , February 15 – 17, 2012, Innsbruck, Austria

track 764 035-
9780889869097 (ISBN)







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