Deep Learning based Model Identification System Exploits the Modular Structure of a Bio-inspired Posture Control Model for Humans and Humanoids

Vittorio Lippi

Abstract

This work presents a system identification procedure based on Convolutional Neural Networks (CNN) for human posture control using the DEC (Disturbance Estimation and Compensation) parametric model. The modular structure of the proposed control model inspired the design of a modular identification procedure, in the sense that the same neural network is used to identify the parameters of the modules controlling different degrees of freedom. In this way the presented examples of body sway induced by external stimuli provide several training samples at once.

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Paper Citation


in Harvard Style

Lippi V. (2021). Deep Learning based Model Identification System Exploits the Modular Structure of a Bio-inspired Posture Control Model for Humans and Humanoids.In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 540-547. DOI: 10.5220/0010245405400547


in Bibtex Style

@conference{icpram21,
author={Vittorio Lippi},
title={Deep Learning based Model Identification System Exploits the Modular Structure of a Bio-inspired Posture Control Model for Humans and Humanoids},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2021},
pages={540-547},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010245405400547},
isbn={978-989-758-486-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Deep Learning based Model Identification System Exploits the Modular Structure of a Bio-inspired Posture Control Model for Humans and Humanoids
SN - 978-989-758-486-2
AU - Lippi V.
PY - 2021
SP - 540
EP - 547
DO - 10.5220/0010245405400547