This paper presents a deep learning enhanced adaptive unscented Kalman filter (UKF) for predicting human arm motion in the context of manufacturing. Unlike previous network-based methods that solely rely on captured human motion data, which is represented as bone vectors in this paper, we incorporate a human arm dynamic model into the motion prediction algorithm and use the UKF to iteratively forecast human arm motions. Specifically, a Lagrangian-mechanics-based physical model is employed to correlate arm motions with associated muscle forces. Then a Recurrent Neural Network (RNN) is integrated into the framework to predict future muscle forces, which are transferred back to future arm motions based on the dynamic model. Given the absence of measurement data for future human motions that can be input into the UKF to update the state, we integrate another RNN to directly predict human future motions and treat the prediction as surrogate measurement data fed into the UKF. A noteworthy aspect of this study involves the quantification of uncertainties associated with both the data-driven and physical models in one unified framework. These quantified uncertainties are used to dynamically adapt the measurement and process noises of the UKF over time. This adaption, driven by the uncertainties of the RNN models, addresses inaccuracies stemming from the data-driven model and mitigates discrepancies between the assumed and true physical models, ultimately enhancing the accuracy and robustness of our predictions. Compared to the traditional RNN-based prediction, our method demonstrates improved accuracy and robustness in extensive experimental validations of various types of human motions.

翻译：本文提出了一种基于深度学习增强的自适应无迹卡尔曼滤波器(UKF)，用于预测制造业中的人体手臂运动。与以往仅依赖捕捉到的人体运动数据（本文中表示为骨骼向量）的基于网络的方法不同，我们将人体手臂动态模型融入运动预测算法，并利用UKF迭代预测人体手臂运动。具体而言，采用基于拉格朗日力学的物理模型关联手臂运动与对应的肌肉力。随后将循环神经网络(RNN)集成到框架中预测未来肌肉力，这些肌肉力再根据动态模型反推回未来手臂运动。针对UKF无法获取未来人体运动测量数据作为状态更新的输入，我们集成了另一个RNN直接预测未来人体运动，并将预测结果作为替代测量数据输入UKF。本研究的一个显著特点是在统一框架中量化数据驱动模型和物理模型的不确定性。这些量化后的不确定性被用于动态调整UKF的测量噪声和过程噪声。这种由RNN模型不确定性驱动的自适应机制，能够修正数据驱动模型产生的不准确性，并缓解假设物理模型与真实模型之间的偏差，最终提升预测的准确性和鲁棒性。与传统基于RNN的预测方法相比，我们的方法在各类人体运动的大量实验验证中展现出更高的准确性和鲁棒性。