Abstract:
Several models have been developed to understand the motor control system of human and animal locomotion. Some of these models are based on psychological backgrounds and some are physiologically inspired. With the present talk a biomechanical model will be introduced which can be used to simulate some aspects of musculoskeletal motion. Especially the stability of such systems should be analyzed with the model.
Considering the large degree of freedom as well as the complexity of our locomotion system it seems to be hopeless to control bipedal walking. Internal and external disturbances increase these difficulties. One strategy to cope with disturbances is to change behavior, i.e. to change the motor program. But thinking about dynamic movements in sports one might imagine the enormous dataflow and the high demands on the accuracy, which are necessary to cope with all variations via the central nervous system.
Therefore it seems to be advantageous to use an 'intelligent' neuro-mechanical system which unburdens the central nervous system. The mechanical system itself should be stable with respect to small perturbations; this intrinsic property of musculoskeletal systems was named self-stability. On the other hand the properties of the neural network within the spinal column itself may support the stabilization of cyclic and acyclic motions.
To discuss stability in a mathematical sense we use the framework of dynamical systems and apply this to biological musculoskeletal systems. To investigate the self-stabilizing properties of single muscle contractions, quick-release contractions can be used as a simple perturbation test. As a next step, it is necessary to analyze the interaction between muscle properties and the geometry of a joint. Here, we are interested in the stabilizing properties of muscles and the skeleton in general.
To discuss the interaction of the neural network within the spinal column we introduce a simple model, which can describe the function of so called spinal pattern generators. These spinal pattern generators are able to generate complex muscular activation patterns, based on simple central commands and feedback from proprioceptive sensors.

Brief Biography of the Speaker:
Dr. Heiko Wagner, is a professor for Biomechanics and Motor Control, at the University of Muenster, Germany, since 2006. He took his PhD in physics at the University of Frankfurt, Department of Theoretical Physics, Germany on 2000, and his habilitation in motion science at the University of Jena, Department of Behavioural Sciences, Germany on 2004.
Since 1996 he worked as a academic assistant at the Institute of Sportscience, Science of Motion, Universtiy of Jena under supervision of Professor Reinhard Blickhan.
His current research interest is the biomechanical analysis of how humans and animals are able to perform highly accurate and stable motions. He developed musculoskeletal models based on biomechanical time-invariant properties, using experimental and analytical methods in biomechanics and nonlinear dynamics.
The focus of his work is to analyse the self-stabilising properties of the musculoskeletal system in human and animal locomotion. Self-stability is based on both the mechanical properties and the learned movement patterns of the humans and animals. Therefore, there is a deep connection between the motor control system, which includes reflexes and inter-muscular co-ordination, and the biomechanics of humans and animals. While trying to understand the biomechanics of motor control, it is necessary to bridge the gap between different scientific fields, i.e. mathematics, physics, biology, biomechanics, medicine and physiotherapy, psychology, robotic engineering and others. Furthermore, we are investigating the kinetics and motor control of patients with chronic back pain.