research-article

SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic Social Networks

Authors:

Elizabeth B. Klerman,

Andrew W. McHill,

Andrew J. K. Phillips,

Akane SanoAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 8, Issue 1

Article No.: 37, Pages 1 - 34

https://doi.org/10.1145/3643508

Published: 06 March 2024 Publication History

Abstract

Sleep behavior significantly impacts health and acts as an indicator of physical and mental well-being. Monitoring and predicting sleep behavior with ubiquitous sensors may therefore assist in both sleep management and tracking of related health conditions. While sleep behavior depends on, and is reflected in the physiology of a person, it is also impacted by external factors such as digital media usage, social network contagion, and the surrounding weather. In this work, we propose SleepNet, a system that exploits social contagion in sleep behavior through graph networks and integrates it with physiological and phone data extracted from ubiquitous mobile and wearable devices for predicting next-day sleep labels about sleep duration. Our architecture overcomes the limitations of large-scale graphs containing connections irrelevant to sleep behavior by devising an attention mechanism. The extensive experimental evaluation highlights the improvement provided by incorporating social networks in the model. Additionally, we conduct robustness analysis to demonstrate the system's performance in real-life conditions. The outcomes affirm the stability of SleepNet against perturbations in input data. Further analyses emphasize the significance of network topology in prediction performance revealing that users with higher eigenvalue centrality are more vulnerable to data perturbations.

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Cited By

Trujillo RZhang ETempleton JPoellabauer C(2024)Predicting long-term sleep deprivation using wearable sensors and health surveysComputers in Biology and Medicine10.1016/j.compbiomed.2024.108749179(108749)Online publication date: Sep-2024
https://doi.org/10.1016/j.compbiomed.2024.108749

Index Terms

SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic Social Networks
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 8, Issue 1

March 2024

1182 pages

EISSN:2474-9567

DOI:10.1145/3651875

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Published: 06 March 2024

Published in IMWUT Volume 8, Issue 1

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Trujillo RZhang ETempleton JPoellabauer C(2024)Predicting long-term sleep deprivation using wearable sensors and health surveysComputers in Biology and Medicine10.1016/j.compbiomed.2024.108749179(108749)Online publication date: Sep-2024
https://doi.org/10.1016/j.compbiomed.2024.108749

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