Dynamic Model Informed Human Motion Prediction Based on Unscented Kalman Filter

Abstract

Human motion prediction is the foundation stone of human–robot collaboration in intelligent manufacturing. The nonlinear and stochastic nature of human motion has made it challenging to predict the motion accurately. Many recent deep-learning-based approaches, e.g., convolutional neural networks or recurrent neural networks (RNNs), have been applied to address this challenge. On the other hand, existing works tend to ignore the importance of human dynamics in motion prediction, especially the effect of muscle force on the motion. This article proposes a novel dynamic model informed motion prediction method. It utilizes an unscented Kalman filter (UKF) to predict the state of the future arm dynamic model such that the future motion of the human arm can be obtained. In particular, the arm dynamic model is developed based on Lagrangian mechanics and represented by differential equations. Embracing the future muscle force predicted by RNN into the differential equations, such a dynamic model is capable of explicitly establishing the intrinsic relation between the future muscle force and the corresponding future arm motion. UKF is leveraged to predict the future joint position and velocity of the human arm based on the dynamic model. Experiments on three motion datasets validate that the proposed prediction method, compared with the traditional RNN-based prediction using skeleton vectors, significantly improves the prediction accuracy regarding elbow and wrist positions.