Optimization of EMG Movement Recognition for use in an Upper Limb Wearable Robot

Abstract

To functionally aid patients suffering from neurological disorders, this paper presents a three degrees-of-freedom (DOF) upper limb wearable robot. In order to provide seamless user assistance, the intention of the wearer must be determined. As a sensing mechanism, electromyographic (EMG) signals have commonly been used to estimate biomechanical human movement. In this study, the effectiveness of movement recognition using a generalized 8-port EMG sensor (Myo Armband) around the forearm was evaluated. Four fundamental movements of the arm (wrist flexion/extension and forearm pronation/supination) were classified against each other and against absence of movement using a neural network with a single hidden layer. The classification method was optimized through analysis of pre-processing algorithms and window size (0.25 to 1 second). The goal was to reduce computational expense and maintain classification accuracy. Through these accomplishments, significant groundwork has been provided for the development of a robust and non-invasive solution to tremor of the upper limb.