小規模人体運動データ解析におけるモデルの重要性：ヒューマノイドとヒトの姿勢制御へのニューロロボティクス応用からの示唆
The Importance of Models in Data Analysis with Small Human Movement Datasets -- Inspirations from Neurorobotics Applied to Posture Control of Humanoids and Humans
外乱推定・補償（DEC）パラメトリックモデルに基づき、CNNでヒトの姿勢制御をシステム同定する手法を提案。モジュール構造を活かし同一ネットワークで異なる自由度のパラメータを同定する。
著者: Vittorio Lippi, Christoph Maurer, Thomas Mergner
分類: cs.RO, cs.LG
原文アブストラクト
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.
