Models of human knee movements for internal signal estimation

This thesis is a contribution to understanding the biomechanics of human knee control. It attempts to provide an improved simulation and predictive model for use in the study of muscle and joint disease; in particular as an aid in investigating the relationship between osteoarthritis (OA) of the knee and the functioning of the associated neuromuscular control system..;A predictive model of the knee is developed using the software MATLAB/Simulink. The muscle electromyography (EMG) signals from the human subject serve as neural inputs and muscle actions are successfully simulated and analysed.;The actuating and regulating roles of muscles are assessed and affirmed to be significant. The finding re-addresses the concepts invoked in the previous studies, which suggested that the knee achieved movements passively during the swing phase without direct muscular control. Instead, force has been simulated for inactive muscles (i.e. passive force) as well as for muscles, which exhibit EMG activity (i.e. active force).;Muscles have also been found to shorten so much that they have failed to develop force despite being activated. While EMG and joint kinematics are commonly used as measures for muscle and joint performance, it is revealed that joint moment, velocity and power could be more reliable in specifying muscle for joint disorder.;The research's major contribution relates to the use of modern non-linear control theory to estimate signals, which are not amenable to direct measurement in human subjects. A control technique, known as sliding mode control, is incorporated into the existing model to enable the neuromuscular control signals to be reconstructed.;The work presented has the long-term objective of identifying the relationship between the neuromuscular control system and the onset of knee OA. It is hoped that pre-osteoarthrotic indicator can be identified earlier to allow intervention to be introduced before joint damage occurs.