The Importance of Models in Data Analysis with Small Human Movement Datasets -- Inspirations from Neurorobotics Applied to Posture Control of Humanoids and Humans
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 42 references that are in the map. First posted on arXiv 4 Feb 2021.
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3 of 42 references.
References · 3
- 2018A Deep Reinforcement Learning Approach for Dynamically Stable Inverse Kinematics of Humanoid Robotscited by 3
- 2018Emergence of Human-comparable Balancing Behaviors by Deep Reinforcement Learningcited by 3
- 2020Deep Learning for Posture Control Nonlinear Model System and Noise Identificationcited by 2
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