VCR: Learning Valid Contextual Representation for Incomplete Wearable Signals

• URL: http://arxiv.org/abs/2605.18837v1
• Date: Wed, 13 May 2026 03:11:39 GMT
• Title: VCR: Learning Valid Contextual Representation for Incomplete Wearable Signals
• Abstract summary: We propose VCR, a self-supervised framework that learns to extract valid representations robust to modality missingness.<n> VCR consistently improves performance and robustness under full, single-missing, and multiple-missing modality settings.
• Score: 26.890638722471902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wearable devices enable continuous health monitoring from multimodal signals, but real-world deployment is hindered by limited labeled data and pervasive sensor incompleteness. While large-scale self-supervised pretraining reduces label dependence, most existing methods assume full modality availability. Current approaches for handling modality missingness often reconstruct entire absent signals, which can encourage hallucinating modality-specific details that are not inferable from the observed sensor signals and degrade robustness. We propose VCR, a self-supervised framework that learns to extract valid representations robust to modality missingness. VCR employs an orthogonal tokenizer to enforce strict orthogonal disentanglement by rectifying latent manifolds and applying a geometric projection, separating each modality into shared semantics and modality-specific residuals. This design preserves complete information integrity while serving as a structural foundation for robust learning under modality missingness. The resulting tokens are processed by a missing-aware mixture-of-experts backbone that adapts to varying patterns of modality availability. By constraining the objective to reconstruct only the shared components of missing modalities, VCR effectively mitigates hallucinations of non-inferable modality-specific details. Across multiple health monitoring tasks, VCR consistently improves performance and robustness under full, single-missing, and multiple-missing modality settings compared with strong supervised and self-supervised baselines.

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