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.

中文翻译：

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基于可穿戴设备的客观睡眠评估深度学习的挑战和机遇

近年来，可穿戴技术和基于机器学习 (ML) 的深度学习 (DL) 方法的交叉凸显了它们在睡眠研究中的潜力。然而，最近发表在《NPJ Digital Medicine》上的一项研究强调了深度学习模型在使用体动记录数据进行睡眠-觉醒分类中的泛化局限性。本文讨论了领域适应和自我监督学习 (SSL) 所带来的一些挑战和机遇，这些创新方法使用大规模未标记数据来增强 DL 模型在睡眠评估中的通用性。这些方法不仅改进了睡眠-觉醒分类，而且有望扩展到更全面的睡眠阶段分类，通过高效且用户友好的可穿戴监测系统有可能推进自动睡眠评估领域。