一个神经网络模型可同时解决惯性运动追踪的四大现实难题。
Dispelling Four Challenges in Inertial Motion Tracking with One Recurrent Inertial Graph-based Estimator (RING)
- 用递归惯性图网络统一建模，适应不同采样率。
- 四重挑战下仍达8.10±1.19度的平均误差。
- 无需微调即可从仿真直接用于真实场景，适合非专业用户。
本文扩展了基于神经网络的递归惯性图估计器（RING），使其能在广泛采样率下通用，并验证其可克服四个现实挑战：磁场不均、传感器与身体部位错位、稀疏传感器布局和非刚性贴附。RING能从惯性数据中估计三段式双铰链运动链的旋转状态，当四类挑战同时存在时，实验平均绝对跟踪误差为8.10±1.19度。该网络在模拟数据上训练，却在真实数据上评估，展现出出色的零样本泛化能力。我们通过消融实验分析各挑战的影响，验证其对采样率变化的鲁棒性，并证明其具备实时运行能力。本研究不仅提升了惯性运动追踪技术的实用性与普适性，还拓展了其在非专业用户、稀疏传感与非刚性贴附等开放环境中的应用前景。
原文摘要 · Abstract (English)
In this paper, we extend the Recurrent Inertial Graph-based Estimator (RING), a novel neural-network-based solution for Inertial Motion Tracking (IMT), to generalize across a large range of sampling rates, and we demonstrate that it can overcome four real-world challenges: inhomogeneous magnetic fields, sensor-to-segment misalignment, sparse sensor setups, and nonrigid sensor attachment. RING can estimate the rotational state of a three-segment kinematic chain with double hinge joints from inertial data, and achieves an experimental mean-absolute-(tracking)-error of 8.10 +/- 1.19 degrees if all four challenges are present simultaneously. The network is trained on simulated data yet evaluated on experimental data, highlighting its remarkable ability to zero-shot generalize from simulation to experiment. We conduct an ablation study to analyze the impact of each of the four challenges on RING's performance, we showcase its robustness to varying sampling rates, and we demonstrate that RING is capable of real-time operation. This research not only advances IMT technology by making it more accessible and versatile but also enhances its potential for new application domains including non-expert use of sparse IMT with nonrigid sensor attachments in unconstrained environments.
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