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.