Human-machine interface in bioprosthesis control using EMG signal classification

We present a concept of human-machine interface intended for the task of bioprosthesis decision control by means of sequential recognition of the patient's intent based on the electromyography (EMG) signal acquired from his/her body. The EMG signal characteristics, the problem of processing the signals including acquisition and feature extraction and their classification are discussed. The contextual (sequential) recognition via fuzzy relations for the classification of the patient's intent is considered and the implied decision algorithms are presented. In the proposed method, the fuzzy relation is determined on the basis of the learning set as a solution of an appropriate optimization problem and then this relation is used in the form of a matrix of membership degrees at successive instants of the sequential decision process. Three algorithms of sequential classification which differ from one another in the sets of input data and procedure are described. The proposed algorithms were experimentally tested in the recognition of phases of the grasping process of the hand on the basis of the EMG signal, where the real-coded genetic algorithm was used as an optimization procedure. The concept of the measurement stand which was the source of information exploited in the experimental investigations of the algorithms is also described.