Deep Learning for Posture Control Nonlinear Model System and Noise Identification

Vittorio Lippi, Thomas Mergner, Christoph Maurer

2020

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. After testing the performance of the CNN on validation and test sets, two examples are presented and discussed from the qualitative point of view: the identification of parameters using data from human experiments and using data of a simulated model with some differences with respect to the one used to build the training set. Some potential implications of the method for humanoid robotics are also discussed.

Paper Citation

in Harvard Style

Lippi V., Mergner T. and Maurer C. (2020). Deep Learning for Posture Control Nonlinear Model System and Noise Identification.In Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-442-8, pages 607-614. DOI: 10.5220/0009148106070614

in Bibtex Style

@conference{icinco20,
author={Vittorio Lippi and Thomas Mergner and Christoph Maurer},
title={Deep Learning for Posture Control Nonlinear Model System and Noise Identification},
booktitle={Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2020},
pages={607-614},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009148106070614},
isbn={978-989-758-442-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Deep Learning for Posture Control Nonlinear Model System and Noise Identification
SN - 978-989-758-442-8
AU - Lippi V.
AU - Mergner T.
AU - Maurer C.
PY - 2020
SP - 607
EP - 614
DO - 10.5220/0009148106070614