The paper presents an original method of recognition of patient's intention to move of hand prosthesis during the grasping and manipulation of objects. The proposed method is based on a 2-level multiclassier system (MCS) with base classifiers dedicated to EMG and MMG signals, and with combining mechanism using a dynamic ensemble selection (DES) scheme and competence function. Competence function of base classifier is determined using validation set in the two step proce- dure. The first step consists in creating competence set using the methods based on relating the response of the classifier with the response obtained by a random guessing. In the second step, the competence set is gener- alized to the whole feature space using the learning procedure based on the Mamdani-type fuzzy inference system. The performance of MCS with proposed competence measure was experimentally compared against four benchmark classification methods using real data concerning the recogni- tion of six types of grasping movements. The system developed achieved the highest classification accuracies demonstrating the potential of MC system for the control of bioprosthetic hand.
['Marek Kurzynski', 'Andrzej Wolczowski']
Multiclassifier System with Fuzzy Inference Method Applied to the Recognition of Biosignals in the Control of Bioprosthetic Hand