Knee joint angle prediction with the use of electromyography

Abstract

Mobility in humans is something which is normally taken for granted, that is until a person's mobility has been impaired. This reduction in mobility can either be caused by an illness or by an accident, and in either case it requires a lot of rehabilitation in order overcome this limitation. This work aims to aid the rehabilitation process by using surface electromyography (sEMG) technology to capture the muscle impulses generated when extending and flexing the knee joint, filtering the signals and then using a back propagation neural network (BPNN) and a time delay neural network (TDNN) to predict the knee angle. This will give patients an indication of the degree of motion that they should be performing, thus enabling their rehabilitation to be better adapted to suit their needs.

To achieve this, different exercises were carried out by a subject and the electrical potentials generated by the muscles recruited were recorded throughout the exercise. Through testing, it was determined that the best exercise carried out was that of flexion and extension while the subject was seated. The signals were then filtered, processed and features were extracted to train and to estimate the knee joint angle by using a neural network. Tests were conducted to determine an appropriate neural network size for best accuracy of the estimated knee joint angle were carried out. The performance of the optimal BPNN and TDNN were compared and it was determined that for flexion and extension of the knee while seated, a TDNN with a delay of 20 gave the best joint angle estimation with an average percentage angular error of 0.3882 percent and actual angular error of 7.2242 degrees, and a correlation coefficient of 0.9659.