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 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 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 10.4% in terms of test MSE, respectively. Furthermore, analysis of the learned representations demonstrates their strong discriminative power,and two downstream application tasks confirm the practical value of our model.

翻译：心率预测对于个性化健康监测和健身至关重要，但在实际部署中经常面临一个关键挑战：数据异构性。我们将其分为两个关键维度：源异构性，源于具有不同特征集的碎片化设备市场；以及用户异构性，反映了不同个体和活动间的独特生理模式。现有方法要么丢弃设备特定信息，要么未能建模用户特定差异，限制了其实际性能。为解决此问题，我们提出了一个学习对两种异构性均保持不变的潜在表征的框架，使下游预测器能够在异构数据模式下一致工作。具体而言，我们引入了一种随机特征丢弃策略来处理源异构性，使模型对各种特征集具有鲁棒性。为管理用户异构性，我们采用了一个历史感知注意力模块来捕获长期生理特征，并使用对比学习目标来构建一个具有判别力的表征空间。为反映现实世界数据的异构性，我们创建了一个新的基准数据集PARROTAO。在PARROTAO和公开的FitRec数据集上的评估表明，我们的模型在测试均方误差方面分别显著优于现有基线17.5%和10.4%。此外，对所学习表征的分析证明了其强大的判别能力，两个下游应用任务也证实了我们模型的实际价值。