用雷达非接触监测睡眠，准确率超90%，适合长期居家使用。
The Breakthrough of Sleep: A Contactless Approach for Accurate Sleep Stage Detection Using the Sleepal AI Lamp
- 通过雷达采集呼吸和体动信号，结合深度学习建模分析睡眠阶段。
- 清醒/睡眠二分类准确率达92.8%，四阶段分类准确率78.5%。
- 无需穿戴设备，适合临床筛查与长期居家睡眠监测。
睡眠分期对评估睡眠质量及诊断睡眠障碍至关重要。传统多导睡眠图（PSG）虽为金标准，但具有侵入性、耗时且不适用于长期监测。本研究评估了名为Sleepal AI Lamp的非接触式雷达睡眠追踪器在大规模数据集（1022例夜间记录）上的表现，该设备基于消费级雷达技术。通过从雷达信号中提取多尺度呼吸与体动特征，训练了一种频域增强的深度学习模型。在清醒/睡眠二分类任务中，模型准确率达到92.8%，宏平均F1得分为0.895；在四阶段分类（清醒、轻度NREM（N1+N2）、深度NREM（N3）、REM）中，健康人群准确率为78.5%，科恩κ系数为0.695；在包含不同严重程度阻塞性睡眠呼吸暂停（OSA）患者的异质人群中，准确率仍保持77.2%，κ系数为0.677。实验结果表明，该非接触式Sleepal AI Lamp的睡眠分期性能与专家标注的PSG高度一致。研究提示，结合先进时序建模的非接触雷达感知，可在无需物理接触或可穿戴设备的情况下实现可靠的睡眠分期。因其无感部署、易安装且具备长期稳定性，该系统在临床筛查、家庭睡眠评估及真实世界医疗中的连续纵向监测中展现出巨大潜力。
原文摘要 · Abstract (English)
Sleep staging is essential for the assessment of sleep quality and the diagnosis of sleep-related disorders. Conventional polysomnography (PSG), while considered the gold standard, is intrusive, labor-intensive, and unsuitable for long-term monitoring. This study evaluates the performance of the Sleepal AI Lamp, a contactless, radar-based consumer-grade sleep tracker, in comparison with gold-standard polysomnography (PSG), using a large-scale dataset comprising 1022 overnight recordings. We extract multi-scale respiratory and motion-related features from radar signals to train a frequency-augmented deep learning model. For the binary sleep-wake classification task, experimental results demonstrated that the model achieved an accuracy of 92.8% alongside a macro-averaged F1 score of 0.895. For four-stage classification (wake, light NREM (N1 + N2), deep NREM (N3), REM), the model achieved an accuracy of 78.5% with a Cohen's kappa coefficient of 0.695 in healthy individuals and maintained a stable accuracy of 77.2% with a kappa of 0.677 in a heterogeneous population including patients with varying severities of obstructive sleep apnea (OSA). These experimental results demonstrate that the sleep staging performance of the contactless Sleepal AI Lamp is in high agreement with expert-labeled PSG sleep stages. Our findings suggest that non-contact radar sensing, combined with advanced temporal modeling, can provide reliable sleep staging performance without requiring physical contact or wearable devices. Owing to its unobtrusive nature, ease of deployment, and robustness to long-term use, the contactless Sleepal AI Lamp shows strong potential for clinical screening, home-based sleep assessment, and continuous longitudinal sleep monitoring in real-world medical and healthcare applications.
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