物理自监督学习：无需人工标签的惯性测量单元感知
Physical Self-Supervised Learning: IMU Sensing without Manual Labels
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中文总结 AI 辅助
研究针对基于IMU传感中数据标注成本高、扩展性受限等问题，提出物理自监督学习范式，通过自适应物理解码器等方法，在无标签情况下实现IMU传感，经实验验证在惯性跟踪和运动捕捉中显著降低误差，优于现有基线。
中文摘要 AI 辅助
深度神经网络已成为基于惯性测量单元（IMU）传感的一种有前景的方法，但其扩展性受昂贵的标注数据限制，且对异构设备、放置位置及用户的鲁棒性较差。现有无监督和自监督方法虽减少但未消除这种依赖，仍需标注数据进行域适应且很大程度上忽略已知物理结构。我们提出物理自监督学习，一种用于无标签IMU传感的自动编码器式范式。用自适应物理解码器取代传统神经解码器，采用混合两阶段IMU编码器在结构化潜在空间中进行重建以减轻传感器噪声。还引入概率频率 - 空间约束来分离传感器和物体运动，利用多视图运动树利用稀疏物理自监督信号，并采用不确定性感知公式处理IMU推理的固有模糊性。在公共数据集和实际部署上进行惯性跟踪和全身运动捕捉评估，在具有挑战性的泛化场景中，物理自监督学习使跟踪误差降低多达5倍，运动捕捉误差降低多达4倍，始终优于无标签的最新监督和自监督基线。
英文摘要
Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly labeled data and poor robustness to heterogeneous devices, placements, and users. Existing unsupervised and self-supervised methods reduce but do not remove this dependence, still requiring labeled data for domain adaptation and largely ignoring known physical structure. We propose physical self-supervised learning, an autoencoder-style paradigm for label-free IMU sensing. We replace the conventional neural decoder with an auto-adaptive physics decoder, a learnable family of kinematic equations that enforces explicit physical structure while adapting across environments, and adopt a hybrid two-stage IMU encoder with reconstruction in a structured latent space to mitigate sensor noise. Our framework further introduces probabilistic frequency-spatial constraints to disentangle sensor and object motion, a multi-view kinematic tree to exploit sparse physical self-supervised signals, and an uncertainty-aware formulation to handle the inherent ambiguity of IMU inference. Evaluated on inertial tracking and full-body motion capture over public datasets and realistic deployments, physical self-supervised learning reduces errors by up to 5x for tracking and 4x for motion capture in challenging generalization scenarios, consistently outperforming state-of-the-art supervised and self-supervised baselines without any labels. Our code is available at https://github.com/YuyangLeng/physical-ssl-imu-label-free
发表机构
- George Mason University（乔治梅森大学）
- Global Technology Applied Research, JPMorgan Chase（摩根大通全球技术应用研究部）
机构由 AI 辅助整理，请以论文原文为准。
