Deep Learning for Posture Control Nonlinear Model System and Noise Identification

TL;DR

This paper introduces a CNN-based system identification method for nonlinear human posture control models, aiming to reduce computational complexity and facilitate clinical and robotic applications.

Contribution

It presents a novel CNN approach for nonlinear system identification in human posture control, improving efficiency over traditional methods.

Findings

CNN effectively identifies nonlinear posture control models

Method reduces computational time for system analysis

Potential applications in clinical testing and humanoid robotics

Abstract

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.