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, _**D**_ ataset for _**R**_ eal-time sleep stage _**E**_ stim _ **A**_ tion using _**M**_ ultisensor wearable _**T**_ echnology, 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.睡眠是人类生命的固有组成部分。近年来，可穿戴技术与机器学习的快速发展为睡眠健康与睡眠模式的连续无创监测提供了可行路径，为全面认知健康福祉开辟了重要维度。然而，仅依托智能手表实现稳定可靠的睡眠分期实时估测仍颇具挑战，对于睡眠节律不规律或存在睡眠障碍的人群而言，这一困境尤为凸显。造成这一困境的核心诱因之一，是当前公开可获取的大规模睡眠数据集显著匮乏，难以支持研究人员与工程师针对睡眠模式多样化的人群，验证其可穿戴睡眠分期算法的有效性。本研究提出DREAMT数据集，全称为**多传感器可穿戴技术实时睡眠分期估测数据集**（Dataset for Real-time Sleep Stage Estimation Using Multisensor Wearable Technology），该数据集采集自100名受试者，包含智能手表采集的高分辨率生理信号、专业睡眠技师标注的睡眠分期标签，以及与睡眠健康和睡眠障碍相关的临床元数据。

搜集汇总

背景与挑战

背景概述

DREAMT是一个专注于实时睡眠阶段估计的数据集，利用多传感器可穿戴技术收集数据，旨在解决智能手表睡眠追踪中可靠性和一致性不足的挑战。该数据集包含100名参与者的高分辨率智能手表信号、专家标注的睡眠阶段标签和临床元数据，为验证可穿戴睡眠算法提供了公开、大规模且覆盖多样睡眠模式的数据资源。