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This work presents a system identification procedure based on ConvolutionalNeural Networks (CNN) for human posture control using the DEC (DisturbanceEstimation and Compensation) parametric model. The modular structure of theproposed control model inspired the design of a modular identificationprocedure, in the sense that the same neural network is used to identify theparameters of the modules controlling different degrees of freedom. In this waythe presented examples of body sway induced by external stimuli provide severaltraining samples at once.

The Importance of Models in Data Analysis with Small Human Movement  Datasets -- Inspirations from Neurorobotics Applied to Posture Control of  Humanoids and Humans