Towards efficient and robust control of bipedal walking: basic models of posture and rhythmic movement

Walking is a very important function of the human movement apparatus. The question how walking is controlled by the central nervous system is yet to be answered. A number of reasons lead us to believe that neural oscillators in the spinal cord, termed Central Pattern Generators (CPGs), have a major contribution to human gait control. Firstly, CPGs play a key role in locomotion of many animals by providing the basic rhythm for muscular activity and by interacting with the reflex system. Secondly, normal walking does not require attention: it goes automatically. Finally, a growing number of observations indicate the presence of CPGs in the human spine. A convincing example of the latter is the fact that anencephalic babies – having a brain stem but no cerebellum or cerebrum – are able to ‘walk’ on a treadmill and display coordinated stepping movements when their feet touch the ground. At present, no bipedal gait model combines efficiency and robustness up to the level of human walking. The main motivation for the research in this thesis is to obtain fundamental knowledge of the principles that account for this reconciliation of efficiency and robustness in human walking. Other motivations come from the fields of rehabilitation and bipedal gait robots. The goal of the conducted research is to find the basic principles of neural control that make human walking both efficient and robust. To achieve this goal, a bottom-up approach was chosen that started with analyzing the behavior and stability of posture under reflexive control and concluded with an efficient and robust spinal control of bipedal gait.