CNNによる姿勢制御の非線形モデル同定と雑音除去
Deep Learning for Posture Control Nonlinear Model System and Noise Identification
人間の姿勢制御モデルのパラメータ同定に畳み込みニューラルネットワーク(CNN)を用い、非線形モデル同定の計算負荷を軽減して臨床応用やヒューマノイドロボットへの可能性を論じた研究。
著者: Vittorio Lippi, Thomas Mergner, Christoph Maurer
分類: cs.RO, cs.LG, stat.ML
原文アブストラクト
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.
