Title: Learning Representations from Heterogeneous Data for Robust Heart Rate Modeling
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Heart Rate Prediction, Rrepresentation Learning

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 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 17.5% and 14.6%, respectively. Furthermore, analysis of the learned representations demonstrates their strong discriminative power, and one downstream application task confirm the practical value of our model.