Context-aware Sleep Analysis with Intraday Steps and Heart Rate Time Series Data from Consumer Activity Trackers

Zilu Liang, Zilu Liang, Huyen Hoang Nhung, Lauriane Bertrand, Nathan Cleyet-Marrel

2022

Abstract

Wearable consumer activity trackers have become a popular tool for longitudinal monitoring of sleep quality. However, sleep data were routinely visualized in isolation from other contextual information. In this paper, we proposed a sleep analytics method to identify the associations between sleep quality and the contextual data that are readily measurable with a single Fitbit device. Different from prior studies that only focused on the daily aggregation of the contextual factors (e.g., total step counts), our method considers the intraday temporal patterns of these factors. Time-domain, frequency-domain, and nonlinear features were derived using the minute-by-minute intraday step and heart rate time series. The results showed that some of the identified contextual features such as the zero-crossing of steps and the absolute energy of heart rate could lead to actionable insights. While the nonlinear features—such as the average and longest diagonal line length derived through the recurrent quantitative analysis of the step time series—may not lead to insights that can be immediately acted on, they generated new hypotheses for further scientific studies. The results also showed that when dealing with data of consumer wearables, the individual-level analysis could generate more personally relevant insight than the cohort-level analysis.

Paper Citation

in Harvard Style

Liang Z., Nhung H., Bertrand L. and Cleyet-Marrel N. (2022). Context-aware Sleep Analysis with Intraday Steps and Heart Rate Time Series Data from Consumer Activity Trackers. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF; ISBN 978-989-758-552-4, SciTePress, pages 170-179. DOI: 10.5220/0010892900003123

in Bibtex Style

@conference{healthinf22,
author={Zilu Liang and Huyen Hoang Nhung and Lauriane Bertrand and Nathan Cleyet-Marrel},
title={Context-aware Sleep Analysis with Intraday Steps and Heart Rate Time Series Data from Consumer Activity Trackers},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF},
year={2022},
pages={170-179},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010892900003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF
TI - Context-aware Sleep Analysis with Intraday Steps and Heart Rate Time Series Data from Consumer Activity Trackers
SN - 978-989-758-552-4
AU - Liang Z.
AU - Nhung H.
AU - Bertrand L.
AU - Cleyet-Marrel N.
PY - 2022
SP - 170
EP - 179
DO - 10.5220/0010892900003123
PB - SciTePress