Classification of EMG Signals of Lower Arm (Forearm\ Hand) Motion Patterns used to Control Robot Hand Movement
Electromyographic (EMG) signals have been widely employed as a control signal in rehabilitation and a means of diagnosis in health care. Signal amplification and filtering is the first step in surface EMG signal processing and application systems. The characteristics of the amplifiers and filters determine the quality of EMG signals. Up until now, searching for better amplification and filtering circuit design that is able to accurately capture the features of surface EMG signals for the intended applications is still challenging. EMG signals are expected to be used and integrated within small or even tiny intelligent, automatic, robotic, and mechatronics systems. This work is focused on small size amplification and filtering circuit design for processing surface EMG signals from an upper limb by using the most common way to control hand prosthesis through the classification of EMG signals extracted from humans. Thus we explore how feasible it is to classify signals acquired by a miniaturized acquisition board and compare extracted EMG signals from a predefined set of four different movements of the lower arm (forearm\ hand) motion patterns using our designed three stage EMG extraction circuit and a MyoWare circuit. These movements are chosen with the aim of producing a valid base for various tasks while involving only a few hand and wrist joints making it possible to design a prosthesis with a reduced number of controlled degrees of freedom, conclusively using EMG voltage values to program various prostheses
Keywords - EMG Signal Analysis, Limb Prosthesis, Classifiers, Forearm.