Optimization of Features to Classify Upper-Limb Movements Through sEMG Signal Processing

Abstract

This paper aims to present the development of a computational intelligence method based on Regularized Logistic Regression able to classify 17 distinguish upper-limb movements through the sEMG signal processing. The choose of the tuning parameters of the regularization and the generation of the different classification methods are presented. For the different models were used variations involving 12 sEMG channels and the RMS, Variance and Medium Frequency features with which we proposed to achieve a most proper combination of parameter to perform the movements classification. The tests involved 50 subjects, including 10 amputees, using the NinaPro database and also a database currently on development by the authors. The global mean accuracy rate considering all the subjects and the channel and features variations was 70,2% prior the definition of the best case scenario. Once we defined the most proper channel and features combination, we were able to improve the accuracy rate to 87,1%, raising the rates of all movements performed for all databases.