论文标题
姿势控制非线性模型系统和噪声识别的深度学习
Deep Learning for Posture Control Nonlinear Model System and Noise Identification
论文作者
论文摘要
在这项工作中，我们提出了一个基于人类姿势控制模型的卷积神经网络（CNN）的系统识别程序。对人类姿势控制的研究的常规方法在于识别控制系统的参数。在这种情况下，由于识别所需参数并分析结果的相对简单性，线性模型特别受欢迎。相反，需要非线性模型来预测人类受试者所表现出的真实行为，因此希望将其用于姿势控制分析。 CNN的使用旨在克服识别非线性模型的繁重计算需求，以使实验数据的分析降低了耗时的时间，并且从角度来看，在临床测试的背景下可行的分析。还讨论了该方法对人形机器人技术的一些潜在影响。
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.
