用稀疏可穿戴数据实现个性化睡眠预测，适应不同人群。
Personalized Sleep Prediction via Deep Adaptive Spatiotemporal Modeling and Sparse Data
- 结合卷积与循环网络捕捉多特征时空关系。
- 输入7天、预测1天时误差最低（RMSE 0.282）。
- 适合睡眠研究、健康监测及可穿戴设备应用。
睡眠预测有助于个人和医疗人员提前识别影响优质睡眠的因素，从而提升身心健康。本文提出一种自适应时空模型（AdaST-Sleep），通过卷积层捕获多变量间的空间特征交互，利用循环神经网络处理长期健康数据，并引入领域分类器实现跨受试者泛化。实验采用五种输入窗口（3、5、7、9、11天）与五种预测窗口（1、3、5、7、9天），结果表明该方法在所有基线模型中表现最优，其中输入7天、预测1天时达到最低均方根误差（RMSE 0.282）。此外，模型在多日预测中仍保持优异性能，具备实际应用潜力。可视化结果显示，模型能准确追踪睡眠评分的整体水平与每日波动。本研究验证了该框架在使用商业可穿戴设备稀疏数据和领域自适应技术下的鲁棒性与灵活性。
原文摘要 · Abstract (English)
A sleep forecast allows individuals and healthcare providers to anticipate and proactively address factors influencing restful rest, ultimately improving mental and physical well-being. This work presents an adaptive spatial and temporal model (AdaST-Sleep) for predicting sleep scores. Our proposed model combines convolutional layers to capture spatial feature interactions between multiple features and recurrent neural network layers to handle longer-term temporal health-related data. A domain classifier is further integrated to generalize across different subjects. We conducted several experiments using five input window sizes (3, 5, 7, 9, 11 days) and five predicting window sizes (1, 3, 5, 7, 9 days). Our approach consistently outperformed four baseline models, achieving its lowest RMSE (0.282) with a seven-day input window and a one-day predicting window. Moreover, the method maintained strong performance even when forecasting multiple days into the future, demonstrating its versatility for real-world applications. Visual comparisons reveal that the model accurately tracks both the overall sleep score level and daily fluctuations. These findings prove that the proposed framework provides a robust and adaptable solution for personalized sleep forecasting using sparse data from commercial wearable devices and domain adaptation techniques.
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