The Importance of Models in Data Analysis with Small Human Movement Datasets -- Inspirations from Neurorobotics Applied to Posture Control of Humanoids and Humans
This work addresses the challenge of analyzing human posture control with small human movement datasets, which is relevant for neurorobotics and biomechanics researchers.
This paper introduces a system identification procedure using Convolutional Neural Networks (CNNs) to analyze human posture control, specifically focusing on the DEC parametric model. The modular design of the control model allows a single neural network to identify parameters for various degrees of freedom, enabling efficient training with multiple samples from body sway experiments.
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.