DREAMT: Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology

Sleep is an intrinsic part of human life, and recent advancements in wearable technology and machine learning have promised continuous and non-invasive methods of tracking sleep health and patterns, providing an important facet to a more holistic understanding of well-being. However, it is still challenging to achieve consistent and reliable real-time estimates of sleep stages using only smartwatches. This is especially true for individuals with irregular sleep patterns or sleep disorders. A major contributing factor is the distinct lack of publicly accessible, large-scale datasets that allow researchers and engineers to validate their wearable sleep staging algorithms against a population with diverse sleep patterns. Here, we present DREAMT, Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology, a new dataset collected from 100 participants, which includes high-resolution signals from a smartwatch, expert sleep technician-annotated sleep stage labels, and clinical metadata related to sleep health and disorders.

睡眠是人类生活的固有组成部分，而可穿戴技术和机器学习的最新进展已承诺提供持续且无创的睡眠健康和模式追踪方法，这为全面理解健康福祉提供了重要的一环。然而，仅凭智能手表实现睡眠阶段的持续和可靠的实时估计仍然充满挑战。这一点对于睡眠模式不规律或患有睡眠障碍的个人而言尤为如此。一个重要的贡献因素是，目前缺乏可供研究人员和工程师验证其可穿戴睡眠阶段算法的针对具有多样化睡眠模式人群的公开可访问的大型数据集。在此，我们呈现DREAMT（使用多传感器可穿戴技术的实时睡眠阶段估计数据集），这是一个从100名参与者中收集的新数据集，其中包含来自智能手表的高分辨率信号、专家睡眠技师标注的睡眠阶段标签以及与睡眠健康和疾病相关的临床元数据。