arXiv — Machine Learning · · 3 min read

Physical Self-Supervised Learning: IMU Sensing without Manual Labels

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Computer Science > Machine Learning

arXiv:2607.18361 (cs)
[Submitted on 20 Jul 2026]

Title:Physical Self-Supervised Learning: IMU Sensing without Manual Labels

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Abstract: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.
Comments: 15 pages, 20 figures. Published in ACM MobiSys 2026
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.18361 [cs.LG]
  (or arXiv:2607.18361v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2607.18361
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Proc. 24th ACM MobiSys, 2026, pp. 1011-1025
Related DOI: https://doi.org/10.1145/3745756.3809252
DOI(s) linking to related resources

Submission history

From: Renyuan Liu [view email]
[v1] Mon, 20 Jul 2026 14:01:10 UTC (7,454 KB)
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