Estimation of Control Parameters in Neuromuscular Skeletal Systems Combined with CNNs and Parametric Identification

In this paper, we introduce a method to estimate the control parameters of the neuromuscular skeletal system by using convolution neural networks (CNNs) and parametric identification together. We selected a human standing attitude control system as an object and conducted measurement experiments with the output of barycenter fluctuation with visual instructions as input. Then, we created an image emphasizing the features of the result and carried out transfer learning with stochastic gradient descent method with CNNs using the AlexNet. Here, we classified the measurement time and the result for each subject into 16 classes. On the other hand, we created a mathematical model of the system and carried out a parametric identification of the control parameters of the neuromuscular skeletal system the standing attitude control system using the ARMAX algorithm. Next, using the feature extraction method, the CNN output, and the original parameter estimation method, the control parameters of the verification data estimated without using ARMAX from the CNN output and the control parameter information of the learning data. As the results, (1) the center of gravity fluctuation differs for each subject and each hour. (2) feature extraction works effectively. (3) It is possible to correlate data classified by CNNs and estimated values of control parameters. We showed these three results. Moreover, from the viewpoint of the system model, it suggested that the decision process of deep learning could be analyzed using the change of internal parameters in the system.