Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling

• URL: http://arxiv.org/abs/2508.21785v2
• Date: Fri, 17 Oct 2025 07:11:23 GMT
• Title: Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling
• Authors: Peng Yang, Zhengdong Huang, Zicheng Xie, Wentao Tian, Jingyu Liu, Lunhong Dong,
• Abstract summary: Heart rate prediction is vital for personalized health monitoring and fitness, but it frequently faces a critical challenge when deploying in real-world data.<n>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.<n>Existing methods either discard device-specific information, or fail to model user-specific differences, limiting their real-world performance.<n>We propose a framework that learns latent representations to both heterogeneity, enabling downstream predictors to work consistently under heterogeneous data patterns.
• Score: 5.538168530326637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Heart rate prediction is vital for personalized health monitoring and fitness, while it frequently faces a critical challenge when deploying in real-world: 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 time-aware attention module to capture long-term physiological traits and use a contrastive learning objective to build a discriminative representation space. To reflect the heterogeneous nature of real-world data, we created and publicly released a new benchmark dataset, ParroTao. Evaluations on both ParroTao and the public FitRec dataset show that our model significantly outperforms existing baselines by 17% and 15%, respectively. Furthermore, analysis of the learned representations demonstrates their strong discriminative power, and one downstream application task confirm the practical value of our model.

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