A bio-mechanical and statistical mechanics analysis of human standing posture

Posturography covers all the techniques used to quantify postural control in upright stance in either static or dynamic conditions. Broadly, this thesis studied (a) static posturography of quiet stance, (b) the partially dynamic posturography of voluntary sway, and (c) posturography of automatic responses preventing falling. The experiments presented in this thesis were carried out by placing the subject in a standing posture on a fixed platform (force plate) instrumented with sensitive force detectors in view of a tracking system, which are able to detect the kinematical and dynamical variations of the body. More specifically, this thesis concerns new studies of human standing posture perturbed by a novel non-mechanical force field (Coriolis force) in a rotating environment. It is shown that rapid motor learning compensates for altered postural dynamics in the rotating environment. The nature and degree of motor learning is shown to be task dependent. We show that the motor learning acquired in a voluntary postural task paradigm transfers to automatic short latency responses to impulsive perturbations. Two novel models are proposed to explain these findings. A simple two leg, shifting pivot model for human stance is examined. A stochastic pinned polymer model is studied and mapped to the dynamics of the quiet stance trajectory in 2-D, and the nature of two point correlation function over short time periods is estimated.