Reactive Hand Movements from Arm Kinematics and EMG Signals Based on Hierarchical Gaussian Process Dynamical Models

Abstract

The prediction of finger kinematics from EMG signals is a difficult problem due to the high level of noise in recorded biological signals. In order to improve the quality of such predictions, we propose a Bayesian inference architecture that enables the combination of multiple sources of sensory information with an accurate and flexible model for the online prediction of high-dimensional kinematics. Our method integrates hierarchical Gaussian process latent variable models (GP-LVMs) for nonlinear dimension reduction with Gaussian process dynamical models (GPDMs) to represent movement dynamics in latent space. Using several additional approximations, we make the resulting sophisticated inference architecture real-time capable. Our results demonstrate that the prediction of hand kinematics can be substantially improved by inclusion of information from the online-measured arm kinematics, and by exploiting learned online generative models of finger kinematics. The proposed architecture provides a highly flexible framework for the integration of accurate generative models with high-dimensional motion in real-time inference and control problems.