Elbow joint angle estimation by using integrated surface electromyography

Abstract

Electromyography (EMG) signals represent the electrical activation of skeletal muscles and contain valuable information about muscular activity. Estimation of the joint movements by using surface EMG signals has great importance as a bio-inspired approach for the control of robotic limbs and prosthetics. However interpreting surface EMG measurements is challenging due to the nonlinearity and user dependency of the muscle dynamics. Hence it requires complex computational methods to map the EMG signals and corresponding limb motions. To solve this challenge we here propose to use an integrated EMG signal to identify the EMG-joint angle relation instead of using common EMG processing techniques. Then we estimate the joint angles for elbow flexion-extension movement by using an auto-regressive integrated moving average with exogenous input (ARIMAX) model, which takes integrated EMG measurements as input. The experiments showed that the suggested approach results in a 21.85% average increase in the estimation performance of the elbow joint angle compared to the standard EMG processing and identification.