Classifying the Finger Movement of a Bionic Hand by Using Emg by Machine Learning

Abstract

The use of the bionic hand requires an extensive training procedure which is a major challenge for patients. The patients need to learn to control the bionic hand before starting using it, therefore, training should be done efficiently. One of the proposed methods is controlling the virtual bionic hand via physical EMG (electromyography) sensors. In general, one of the main problems of any prosthesis is the classification of the patient's finger movements. For this reason, some well-known machine learning algorithms are discussed. Comparative analysis of machine learning algorithms is performed, the best-selected algorithm is used for the system later. The classifier for finger movement classification is trained and tested. The virtual model of the bionic hand has been developed. The kinematics of bionic fingers is analyzed. The bionic finger performs tasks in the Cartesian space, whereas actuators work in the Quaternion (joint) space. It is necessary to transform the coordinate system from Cartesian to joint space and vice versa․ The inverse and forward kinematics is obtained by using the geometry approach and the Denavit - Hartenberg (DH) methods accordingly. The control system is designed for the virtual bionic hand model. The developed method gives an opportunity to classify all the movements of fingers via two surface EMG electrodes with an ML (Machine learning) based or the NN (Neural Network) classifier, and to control the designed bionic hand model in the MATLAB / Simulink environment.