Deep Learning for Posture Control Nonlinear Model System and Noise Identification

DOI10.5220/0009148106070614zbMATH Open1530.93070arXiv2006.04515MaRDI QIDQ6071244

Author name not available (Why is that?), Christoph Mäurer, Author name not available (Why is that?)

Publication date: 22 November 2023

Published in: Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics (Search for Journal in Brave)

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.

Full work available at URL: https://arxiv.org/abs/2006.04515

zbMATH Keywords

system identificationparametric nonlinear systemposture controldeep learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) System identification (93B30) Nonlinear systems in control theory (93C10)

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