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.

睡眠乃人类生命之内在组成部分，近期可穿戴技术与机器学习的进步已承诺提供连续且非侵入式的睡眠健康与模式追踪方法，这对于更全面地理解健康状态具有重要价值。然而，仅利用智能手表实现睡眠阶段的持续且可靠的实时估计仍然是一项挑战。对于睡眠模式不规律或患有睡眠障碍的个人而言，此问题尤为突出。一个主要贡献因素是，目前缺乏公开可获取的大型数据集，使得研究人员与工程师难以将其可穿戴睡眠阶段算法与具有多样化睡眠模式的群体进行验证。鉴于此，我们在此呈现DREAMT（多传感器可穿戴技术实时睡眠阶段估算数据集），这是一项从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。

搜集汇总

背景与挑战

背景概述

DREAMT数据集是一个专注于实时睡眠阶段估计的多传感器可穿戴技术数据集，包含来自100名参与者的高分辨率智能手表信号和专家注释的睡眠阶段标签。该数据集旨在解决使用可穿戴设备进行睡眠监测的挑战，特别针对睡眠模式不规律或睡眠障碍的个体，为开发可靠的睡眠阶段估计算法提供多样化的基准数据。