Surface EMG signals based elbow joint torque prediction

Abstract

Control of transhumeral prosthetic devices can effectively be performed using the predicted joint torques at the elbow. The joint torque values are generally predicted using the Electromyography (EMG) signals taken from upper arm muscles of the amputee. This paper uses a Bagnoli-16 EMG system to extract EMG signals from the biceps and triceps. The EMG signals are complex to handle mainly due to the stochastic nature of the signal. Independent component analysis (ICA) is utilized to isolate the EMG signals from each muscle. In order to measure the actual torque, a novel kinematic model is proposed in this paper. For the joint torque prediction two classifiers have been developed. First an Artificial Neural Network model (ANN) based classifier is trained to predict the joint torques. Using different test data the ANN model is tested against the arm kinematic based joint torque predictions. The test results indicated 5.6% of root mean square error against the actual predicted torque values. In order to improve the classification an Artificial Neuro-Fuzzy inference system (ANFIS) has been developed. Using the same data the ANFIS based classifier produced 3.3% of the root mean square error against the kinematically predicted joint torques.