用5个惯性传感器实现人体全身运动的高精度物理约束预测
Physics-Informed Learning for Human Whole-Body Kinematics Prediction via Sparse IMUs
- 融合物理规律的神经网络，利用5个惯性传感器数据建模
- 在未知受试者上仍保持高精度与自然运动过渡
- 适合需要实时、可靠人体运动预估的机器人协作场景
精准且符合物理规律的人体运动预测对人机安全协作至关重要。尽管当前人体动作捕捉技术可实现实时姿态估计，但多数方法受限于缺乏未来动作预测及物理约束考虑。传统预测方法严重依赖历史姿态，在实际场景中难以获取。为此，本文提出一种融合领域知识的物理感知学习框架，仅使用5个惯性测量单元（IMUs）实现人体全身运动预测。我们设计的网络考虑了人体运动的空间特性；训练阶段引入前向与微分运动学函数作为额外损失项，以正则化关节预测；推理阶段通过更新关节状态缓冲区，将前一时刻预测结果作为额外输入进行迭代优化。实验表明，该方法在未见受试者上表现优异，具备高精度、平滑的动作过渡和良好泛化能力。
原文摘要 · Abstract (English)
Accurate and physically feasible human motion prediction is crucial for safe and seamless human-robot collaboration. While recent advancements in human motion capture enable real-time pose estimation, the practical value of many existing approaches is limited by the lack of future predictions and consideration of physical constraints. Conventional motion prediction schemes rely heavily on past poses, which are not always available in real-world scenarios. To address these limitations, we present a physics-informed learning framework that integrates domain knowledge into both training and inference to predict human motion using inertial measurements from only 5 IMUs. We propose a network that accounts for the spatial characteristics of human movements. During training, we incorporate forward and differential kinematics functions as additional loss components to regularize the learned joint predictions. At the inference stage, we refine the prediction from the previous iteration to update a joint state buffer, which is used as extra inputs to the network. Experimental results demonstrate that our approach achieves high accuracy, smooth transitions between motions, and generalizes well to unseen subjects
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