Dynamic Model Informed Human Motion Prediction Based on Unscented Kalman Filter
Dynamic Model Informed Human Motion Prediction Based on Unscented Kalman Filter
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DOI：
10.1109/tmech.2022.3173167
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Wansong Liu;Xiao Liang;Minghui Zheng
中科院分区：
文献类型：
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作者：
Wansong Liu;Xiao Liang;Minghui Zheng
人类运动预测是人类 - 机器人在智能制造中的合作的基础。另一方面，神经网络（RNN）已应用于这一挑战。本文的力量提出了一种新型动态模型的动态预测方法，手臂动态模型是基于Lagrangian力学开发的，并以微分方程为代表。动态模型能够明确建立未来的肌肉力与相应的未来手臂运动之间的内在关系。与使用骨架向量的传统基于RNN的预测相比，提出的预测方法显着提高了肘部和腕部位置的预测准确性。
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.