TILES-2018 Sleep Benchmark Dataset: A Longitudinal Wearable Sleep Data Set of Hospital Workers for Modeling and Understanding Sleep Behaviors

TL;DR

The TILES-2018 Sleep Benchmark dataset offers a comprehensive, longitudinal collection of wearable sleep data from hospital workers, enabling research on sleep patterns, quality, and behavior modeling in natural settings.

Contribution

This paper introduces a publicly available, large-scale sleep dataset collected via wearable sensors over 10 weeks, with detailed analyses and machine learning benchmarks for sleep research.

Findings

Over 6,000 sleep recordings and survey data collected from 139 hospital workers.

Machine learning models achieved baseline performance on sleep stage classification and sleep quality prediction.

The dataset facilitates advanced modeling of sleep behaviors in real-world environments.

Abstract

Sleep is important for everyday functioning, overall well-being, and quality of life. Recent advances in wearable sensing technology have enabled continuous, noninvasive, and cost-effective monitoring of sleep patterns in real-world natural living settings. Wrist-worn devices, in particular, are capable of tracking sleep patterns using accelerometers and heart rate sensors. To support sleep research in naturalistic environments using wearable sensors, we introduce the TILES-2018 Sleep Benchmark dataset, which we make publicly available to the research community. This dataset was collected over a 10-week period from 139 hospital employees and includes over 6,000 unique sleep recordings, alongside self-reported survey data from each participant, which includes sleep quality, stress, and anxiety among other measurements. We present in-depth analyses of sleep patterns by combining the TILES-2018 Sleep Benchmark dataset with a previously released dataset (TILES-2018), which follows a similar study protocol. Our analyses include sleep duration, sleep stages, and sleep diaries. Moreover, we report machine learning benchmarks using this dataset as a testbed for tasks including sleep stage classification, prediction of self-reported sleep quality, and classifying demographics. Overall, this dataset provides a valuable resource for advancing foundational studies in sleep behavior modeling.