Linear discriminant analysis versus artificial neural network as classifiers for elbow angular position recognition purposes

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Castro M.C.F.
BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing
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The increasing popularity of an Artificial Neural Network for pattern recognition and the absence of comparative studies showing its real superiority over Discriminant Analysis Methods motivated the present study, aiming at comparing the accuracy levels achieved for a Feed-Forward Multilayer Perceptron (MLP) and a Linear Discriminant Analysis (RLDA) applied to myoelectric signals to classify elbow angular positions. The results showed that there were no significant differences (t-student test p<0.05) between the average classification accuracies achieved for both methods even with the search of configuration parameters more appropriate to each situation. Both methods achieved average classification accuracies above 80% for a number of classes up to 4. However, 5 subjects achieved good results in a 5-class setup, which means a 20o shift between consecutive classes. Considering that for MLP there is an effort to define the architecture parameters and also learning parameters, its use is only justified if there is a need of generalization that cannot be achieved by the RLDA that does not require the predefinition of parameters, it is practical and fast, and performs very well.