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In this paper the problem of recognition of patient’s intent to move hand prosthesis is addressed. The proposed method is based on recognition of electromiographic (EMG) and mechanomiographic (MMG) biosignals using a multiclassifier system (MCS) working with dynamic ensemble selection (DES) scheme and original concept of competence function. The competence measure is based on relating the response of the classifier with the decision profile of a test object which is evaluated using K nearest objects from the validation set (static mode). Additionally, feedback information coming from bioprosthesis sensors on the correct/incorrect classification is applied to the adjustment of the combining mechanism during MCS operation through adaptive tuning competences of base classifiers depending on their decisions (dynamic mode). Experimental investigations using real data concerning the recognition of five types of grasping movements and computer-simulated procedure of generating feedback signals are performed. The performance of MCS with the proposed competence measure is experimentally compared against 5 state-of-art MCS’s in static mode and furthermore the MCS system developed is evaluated with respect to the effectiveness of the procedure of tuning competence. The results obtained indicate that the modification of competence of base classifiers during the working phase essentially improves performance of the MCS system. The system developed achieved the highest classification accuracy demonstrating the potential of MCS with feedback signals from prosthesis sensors for the control of bioprosthetic hand.

Multiclassifier System with Dynamic Model of Classifier Competence Applied to the Control of Bioprosthetic Hand