The Importance of Models in Data Analysis with Small Human Movement Datasets -- Inspirations from Neurorobotics Applied to Posture Control of Humanoids and Humans

TL;DR

This paper introduces a CNN-based system identification method for human posture control, leveraging a modular model inspired by neurorobotics, effectively handling small datasets with multiple degrees of freedom.

Contribution

It proposes a novel modular neural network approach for identifying parameters in posture control models, inspired by neurorobotics, suitable for small datasets.

Findings

Effective identification of posture control parameters

Modular neural network structure reduces data requirements

Applicable to both humanoid robots and humans

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.