Deep Learning for Posture Control Nonlinear Model System and Noise Identification
This work addresses the computational bottleneck in analyzing human posture control for clinical and robotics applications, though it appears incremental as it applies an existing method (CNNs) to a specific domain problem.
The authors tackled the problem of identifying parameters for nonlinear human posture control models, which are computationally expensive, by using Convolutional Neural Networks (CNNs) to reduce computational requirements and enable faster analysis of experimental data, potentially making it feasible for clinical tests.
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.