Two-Stage Multiclassifier System with Correction of Competence of Base Classifiers Applied to the Control of Bioprosthetic Hand

Abstract

The paper presents an advanced method of recognition of patient's intention to move hand prosthesis during the grasping and manipulation of objects in a dexterous manner. The proposed method is based on recognition of electromiographic (EMG) and mechanomiographic (MMG) bio signals using two-stage hierarchical multiclassifier system (MCS) with dynamic ensemble selection scheme (DES) and probabilistic competence function. Additionally, the feedback signals derived from the prosthesis sensors are applied to the correction of competences of base classifiers during MCS operation. The performance of proposed MCS was experimetally compared against MCS's without feedback information and with one-stage structure using real data concerning the recognition of five types of grasping movements. The system developed achieved the highest classification accuracy demonstrating the potential of two-stage MCS with feedback signals from prosthesis sensors for the control of bio prosthetic hand.