1 research outputs found
Deep Learning for Posture Control Nonlinear Model System and Noise Identification
In this work we present a system identification procedure based on
Convolutional Neural Networks (CNN) for human posture control models. A usual
approach to the study of human posture control consists in the identification
of parameters for a control system. In this context, linear models are
particularly popular due to the relative simplicity in identifying the required
parameters and to analyze the results. Nonlinear models, conversely, are
required to predict the real behavior exhibited by human subjects and hence it
is desirable to use them in posture control analysis. The use of CNN aims to
overcome the heavy computational requirement for the identification of
nonlinear models, in order to make the analysis of experimental data less time
consuming and, in perspective, to make such analysis feasible in the context of
clinical tests. Some potential implications of the method for humanoid robotics
are also discussed.Comment: to be published in ICINCO 2020, Camera ready versio