Challenges and opportunities of deep learning for wearable-based objective sleep assessment

Bing Zhai, Greg J. Elder, Alan Godfrey*

*Corresponding author for this work

Research output: Contribution to journalEditorialpeer-review

3 Downloads (Pure)

Abstract

In recent years the intersection of wearable technologies and machine learning (ML) based deep learning (DL) approaches have highlighted their potential in sleep research. Yet, a recent study published in NPJ Digital Medicine highlights the generalization limitations of DL models in sleep-wake classification using actigraphy data. Here, this article discusses some of the challenges and opportunities presented by domain adaptation and self-supervised learning (SSL), innovative methodologies that use large-scale unlabeled data to bolster the generalizability of DL models in sleep assessment. These approaches not only improve sleep-wake classification but also hold promise for extending to more comprehensive sleep stage classification, potentially advancing the field of automated sleep assessment through efficient and user-friendly wearable monitoring systems.
Original languageEnglish
Article number85
Pages (from-to)1-3
Number of pages3
Journalnpj Digital Medicine
Volume7
Issue number1
DOIs
Publication statusPublished - 4 Apr 2024

Cite this