Appropriately complex modelling of healthy human walking

Abstract

Modelling human gait has become an invaluable tool in a wide range of fields such as robotics and rehabilitation. With progress in computing, model complexity has advanced quickly but nevertheless, the contributions of incremental increases in model complexity are poorly understood. This thesis addresses this through a series of modelling studies. The first investigation examined the advantages and disadvantages of inverted pendulum (IP) models of walking, using a forward dynamics approach, by comparing to a normal set of experimental gait data. It was shown that the biggest failing of these models is their inability to adequately simulate double stance. The second investigation sought to highlight the effects of additional model complexities on the kinematics and kinetics, using optimisation. The additions, added one-by-one, were a knee joint, an ankle and static foot, a moving foot and a swing leg. The presence of a knee joint and an ankle moment were shown to be largely responsible for the initial peak in the vertical ground force reaction (GRF) curve. The second peak in this curve was achieved through a combination of heel rise and the presence of a swing leg. This gave mathematical evidence for the true determinants of human gait. A double support model was produced next, using a novel method to constrain both feet to the ground and calculate the GRF distribution. This was run in conjunction with the best single support model to simulate a whole gait cycle. Despite the problem of discontinuities at the transitions between double and single support, the whole gait cycle simulation had mean kinematic and mean GRF errors of less than a single standard deviation from the normal experimental data set. The final study collected gait and anthropometric data from ten subjects, which was then applied to the full gait cycle model. The model was shown to be adaptable to different people; a property that would be important for any computational model to be used in clinical assessment and diagnostics.