  This work presents a system identification procedure based on Convolutional
Neural Networks (CNN) for human posture control using the DEC (Disturbance
Estimation and Compensation) parametric model. The modular structure of the
proposed control model inspired the design of a modular identification
procedure, in the sense that the same neural network is used to identify the
parameters of the modules controlling different degrees of freedom. In this way
the presented examples of body sway induced by external stimuli provide several
training samples at once.
