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[2508.21785] Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling
Summary
This paper presents a novel framework for heart rate modeling that addresses data heterogeneity by learning unified representations from diverse data sources, enhancing prediction accuracy.
Why It Matters
Heart rate monitoring is crucial for health and fitness, yet existing models struggle with data diversity. This research introduces a robust solution that improves prediction reliability across various devices and user profiles, potentially transforming personalized health monitoring.
Key Takeaways
- Introduces a framework that learns representations agnostic to data heterogeneity.
- Utilizes random feature dropout to enhance robustness against varying feature sets.
- Employs a history-aware attention module to capture individual physiological traits.
- Demonstrates significant performance improvements over existing models on benchmark datasets.
- Creates a new dataset, PARROTAO, to evaluate heart rate prediction models.
Computer Science > Machine Learning arXiv:2508.21785 (cs) [Submitted on 29 Aug 2025 (v1), last revised 24 Feb 2026 (this version, v3)] Title:Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling Authors:Zhengdong Huang, Zicheng Xie, Wentao Tian, Jingyu Liu, Lunhong Dong, Peng Yang View a PDF of the paper titled Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling, by Zhengdong Huang and Zicheng Xie and Wentao Tian and Jingyu Liu and Lunhong Dong and Peng Yang View PDF HTML (experimental) Abstract:Heart rate prediction is vital for personalized health monitoring and fitness, while it frequently faces a critical challenge in real-world deployment: data heterogeneity. We classify it in two key dimensions: source heterogeneity from fragmented device markets with varying feature sets, and user heterogeneity reflecting distinct physiological patterns across individuals and activities. Existing methods either discard device-specific information, or fail to model user-specific differences, limiting their real-world performance. To address this, we propose a framework that learns latent representations agnostic to both heterogeneity,enabling downstream predictors to work consistently under heterogeneous data patterns. Specifically, we introduce a random feature dropout strategy to handle source heterogeneity, making the model robust to various feature sets. To manage user heterogeneity, we employ a history-aware a...
