Estimation of elbow angle using surface electromyographic signals

Abstract

Disability badly affects the performance of daily activities of a person thereby degrading the quality of his life. Physical rehabilitation can help restoration of the physical functions of such persons to a great extent. Numerous research are being performed for developing muscle models capable of estimating torque, joint angles, force, velocity etc. especially for upper and lower limb kinematics. This paper proposes three models for estimating elbow angle namely, Average Value model, a feed forward back-propagation neural network model and a Non-linear Auto-Regressive with eXogenous input (NARX) neural network model. All these models are studied with the sEMG signals acquired from the biceps brachii muscles of ten healthy subjects. The linear envelope of the signal is utilized for constructing two time domain features which serve as inputs to the models. The output of the models is the estimated elbow angles corresponding to the human intention identified from the sEMG signals. Regression coefficient values and Root mean square error values are considered for evaluating the accuracies of the models. The results obtained show that the models can be effectively used for implementation in control of human limb prosthetics and assistive devices.