Muscle activation based estimation of Knee joint angle using Surface Electromyography Signals

Abstract

The efficacy of using surface electromyography (sEMG) signals to understand the biomechanics of movement has been an active area of research. The need for a reliable standard to monitor/predict rehabilitative outcomes and an efficient control to drive orthotics and exoskeletons has motivated this work to use sEMG as an estimate for joint angle prediction. This work involves acquisition of 8 channel sEMG signals from lower extremity during knee articulation, involving flexion and extension for 20 able bodied subjects. Simultaneous knee joint angle measurement from goniometer is used to train a Back Propagation Neural Network (BPNN) to estimate knee Range of motion (ROM) with Root Mean Square values of 8 sEMG signals as input. Knee joint angles at full ROM for flexion $(90.9\pm 2.4)$ and extension $(19.1 \pm 2.6)$ were obtained. Estimation accuracy based on average Mean square error for flexion (0.146 $\pm 0.197$ ) and extension $(0.098\pm 0.129)$ ) with correlation factor (r) between actual and estimated values were found to be 0.93 and 0.95 respectively. Estimated flexion and extension angles were used to actuate the knee joint of a Virtual Human model. The results suggest that a high degree of correlation can be achieved between sEMG and kinematic measurements.