EMG-based estimation of knee joint angle under functional electrical stimulation using an artificial neural network

In this paper, an artificial neural network is proposed to estimate knee joint angle in hybrid activation of knee extension motion, including voluntary muscle contraction and functional electrical stimulation (FES) induced contraction. Voluntary electromyography (EMG) signals of three muscles responsible for knee extension and FES parameter which describe the FES intensity are used as input vector of the neural network, while the estimated knee angle is the output. During the experiment, FES with different combinations of parameters (pulse amplitude and pulse width) was delivered to the rectus femoris muscle of a healthy male subject when the knee was in a periodic extension motion by voluntary muscle contraction. Raw EMG signals of three muscles, parameters of FES as well as the actual knee angle were recorded. Totally, there were 52,233 and 17,420 sampling points corresponding to 261 and 87 seconds used to train and validate the neural network. The result shows the trained network has a satisfactory performance on knee joint angle estimation whose output well follows the curve of actual knee angle. Root mean square error between estimated angle and actual angle is employed to represent the estimation accuracy which is 5.07 degree according to the experimental data.