VCR: Learning Valid Contextual Representation for Incomplete Wearable Signals
The paper presents VCR, a self-supervised framework designed to handle incomplete wearable signals. It aims to improve health monitoring by extracting valid representations while addressing the challenges of modality missingness. The proposed method demonstrates enhanced performance and robustness across various health monitoring tasks compared to existing approaches.
- ▪VCR employs an orthogonal tokenizer to ensure strict orthogonal disentanglement of modalities.
- ▪The framework mitigates hallucinations of non-inferable modality-specific details by reconstructing only shared components of missing modalities.
- ▪VCR consistently outperforms strong supervised and self-supervised baselines in multiple health monitoring tasks.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18837 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Machine Learning arXiv:2605.18837 (cs) [Submitted on 13 May 2026] Title:VCR: Learning Valid Contextual Representation for Incomplete Wearable Signals Authors:Yuxuan Weng, Wenhan Luo, Qijia Shao View a PDF of the paper titled VCR: Learning Valid Contextual Representation for Incomplete Wearable Signals, by Yuxuan Weng and 2 other authors View PDF HTML (experimental) 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.
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