# Deep Learning for Posture Control Nonlinear Model System and Noise Identification

arXiv: https://arxiv.org/abs/2006.04515 · HTML: https://mlsanity.com/paper/2006.04515/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [Deep Learning Based Model Identification System Exploits the Modular Structure of a Bio-Inspired Posture Control Model for Humans and Humanoids](https://mlsanity.com/paper/2102.02536/) (2021)
- [The Importance of Models in Data Analysis with Small Human Movement Datasets -- Inspirations from Neurorobotics Applied to Posture Control of Humanoids and Humans](https://mlsanity.com/paper/2102.02543/) (2021)

## Abstract (arXiv)

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.