Finger Movement Classification via Machine Learning using EMG Armband for 3D Printed Robotic Hand

Abstract

Millions of people lose their limbs due to accidents, infections and/or wars. While prosthetics are the best solution for amputees, designing autonomous prosthetic hand that can perform major operations is a complicated task and thus the prosthetic hands that are designed are very expensive and also a bit heavy. The biggest challenge in designing a prosthetic hand is the classification of EMG signals generated by neurons in the arm to distinguish finger movements. These EMG signals vary in strength from person to person and from movement to movement. This thesis proposes a computationally efficient way that uses Machine Learning to classify 5 and 12 finger movements from EMG signals captured by a device called “Myo Gesture Control Armband”. Further, an ergonomic design of robotic hand is also presented that is small, lightweight and cheap, designed using a 3D printer.