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Towards Accurate and Efficient Sleep Period Detection Using Wearable Devices

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Computer Analysis of Images and Patterns (CAIP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14185))

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Abstract

Sleep monitoring has traditionally required expensive equipment and expert assessment. Wearable devices are however becoming a viable option for monitoring sleep. This study investigates methods for autonomously identifying sleep segments base on wearable device data. We employ and evaluate machine and deep learning models on the benchmark MESA dataset, with results showing that they outperform traditional methods in terms of accuracy, F1 score, and Matthews Correlation Coefficient (MCC). The most accurate model, namely Light Gradient Boosting Machine, obtained an F1 score of 0.93 and an MCC of 0.73. Additionally, sleep quality metrics were used to assess the models. Furthermore, it should be noted that the proposed approach is device-agnostic, and more accessible and cost-effective than the traditional polysomnography (PSG) methods.

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Notes

  1. 1.

    Available at https://sleepdata.org/datasets/mesa.

  2. 2.

    https://github.com/joaopalotti/sleep_boundary_project.

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Author information

Authors and Affiliations

  1. University of Groningen, Groningen, The Netherlands

    Fatemeh Jokar & George Azzopardi

  2. Earkick GmbH, Zurich, Switzerland

    Joao Palotti

Authors
  1. Fatemeh Jokar
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  2. George Azzopardi
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  3. Joao Palotti
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Corresponding author

Correspondence to Fatemeh Jokar .

Editor information

Editors and Affiliations

  1. Cyprus University of Technology, Limassol, Cyprus

    Nicolas Tsapatsoulis

  2. Cyprus University of Technology/CYENS Center of Excellence, Limassol, Cyprus

    Andreas Lanitis

  3. The University of New Mexico, Albuquerque, NM, USA

    Marios Pattichis

  4. University of Cyprus/CYENS Center of Excellence, Nicosia, Cyprus

    Constantinos Pattichis

  5. University of Cyprus/KIOS Center of Excellence, Nicosia, Cyprus

    Christos Kyrkou

  6. Cyprus University of Technology, Limassol, Cyprus

    Efthyvoulos Kyriacou

  7. Cyprus University of Technology/CYENS Center of Excellence, Limassol, Cyprus

    Zenonas Theodosiou

  8. CYENS Center of Excellence, Nicosia, Cyprus

    Andreas Panayides

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Jokar, F., Azzopardi, G., Palotti, J. (2023). Towards Accurate and Efficient Sleep Period Detection Using Wearable Devices. In: Tsapatsoulis, N., et al. Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14185. Springer, Cham. https://doi.org/10.1007/978-3-031-44240-7_5

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  • DOI: https://doi.org/10.1007/978-3-031-44240-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44239-1

  • Online ISBN: 978-3-031-44240-7

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