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Authors: Vittorio Lippi 1 ; Thomas Mergner 2 and Christoph Maurer 2

Affiliations: 1 Technical University Berlin, Control Systems, Berlin, Germany ; 2 Neurological University Clinic, University of Freiburg, Freiburg im Breisgau, Germany

Keyword(s): Posture Control, Deep Learning, System Identification, Parametric Nonlinear System.

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: th e 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. (More)

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Paper citation in several formats:
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 - ICINCO; ISBN 978-989-758-442-8; ISSN 2184-2809, SciTePress, pages 607-614. DOI: 10.5220/0009148106070614

@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 - ICINCO},
year={2020},
pages={607-614},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009148106070614},
isbn={978-989-758-442-8},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - ICINCO
TI - Deep Learning for Posture Control Nonlinear Model System and Noise Identification
SN - 978-989-758-442-8
IS - 2184-2809
AU - Lippi, V.
AU - Mergner, T.
AU - Maurer, C.
PY - 2020
SP - 607
EP - 614
DO - 10.5220/0009148106070614
PB - SciTePress