Dynamic selection of classifiers ensemble applied to the recognition of EMG signal for the control of bioprosthetic hand

In the paper the problem of EMG-based recognition of user intent for the control of bio-prosthetic hand is addressed. The multiple classifier systems (MCS) with dynamic ensemble selection (DES) strategy based on the original concept of competence measure are applied. In the proposed method first a probabilistic reference classifier (RRC) is constructed which - on average - acts like the classifier evaluated. Next, the competence of the classifier is calculated as the probability of correct classification of the respective RRC. The performace of two MCSs with proposed competence functions were experimetally compared against four benchmark MCSs using real data concerning the recognition of seven types of grasping movements. The systems developed achieved the highest classification accuracies demonstrating the potential of DES systems with competence mesure for recognition of EMG signals.