Abstract:
Postural control is the ability to maintain equilibrium by keeping or returning the center of
gravity (COG) over its base of support (BOS), and it relates to how the body’s position in
space controls for stability. The center of gravity (COG) is a point at which all an object’s
mass can be concentrated in relation to gravity. The postural control system serves as a
feedback control circuit between the brain and the musculoskeletal system. The internal
dynamics of a system model are one of the major functional components that the posture
control system relies on. So, the modeling of CNS will be represented by an extended high
gain observer (EHGO) which is based on a feedback linearization controller. Basically,
EHGO works as a disturbance estimator and a soft sensor of the internal dynamics,
respectively. Moreover, AI approach contributes to a better knowledge of the postural
control and STS mechanism. Second part of this research focus on traditional machine
learning approach used to improve robotic and exoskeleton design. By using head positions
of different experimental objects, regression model will predict the positions of ankle,
knee, and hip joints. Therefore, on head positions defned as input and position of joints
are outputs of the model. In this research supervised learning is used because inputs and
outputs are defned or known. So, the techniques used under supervised learning are
random forest regression, support vector regression (SVM), decision tree regression.
