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BiHeartS: Bilateral Heart Rate from multiple devices and body positions for Sleep measurement Dataset

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arxiv 2308.06811 v1 pith:RSARICUJ submitted 2023-08-13 cs.HC

classification cs.HC
keywords sleepqualitydatasetdailydatadevicesusersbehaviour
verification ladder T0 review T1 audit T2 compute T3 formal
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Sleep is the primary mean of recovery from accumulated fatigue and thus plays a crucial role in fostering people's mental and physical well-being. Sleep quality monitoring systems are often implemented using wearables that leverage their sensing capabilities to provide sleep behaviour insights and recommendations to users. Building models to estimate sleep quality from sensor data is a challenging task, due to the variability of both physiological data, perception of sleep quality, and the daily routine across users. This challenge gauges the need for a comprehensive dataset that includes information about the daily behaviour of users, physiological signals as well as the perceived sleep quality. In this paper, we try to narrow this gap by proposing Bilateral Heart rate from multiple devices and body positions for Sleep measurement (BiHeartS) dataset. The dataset is collected in the wild from 10 participants for 30 consecutive nights. Both research-grade and commercial wearable devices are included in the data collection campaign. Also, comprehensive self-reports are collected about the sleep quality and the daily routine.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Exploring the Alignment of Perceived and Measured Sleep Quality with Working Memory using Consumer Wearables

    cs.HC 2025-05 conditional novelty 5.0 of 10

    Daily self-rated sleep quality in 29 adults correlates weakly with Oura-derived changes in REM sleep, heart rate, bedtime, and working memory scores, and the alignment differs across three user groups.

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