Sleep quality is an important determinant of human health and wellbeing. Novel technologies that can quantify sleep quality at scale are required to enable the diagnosis and epidemiology of poor sleep. One important indicator of sleep quality is body posture. In this paper, we present the design and implementation of a non-contact sleep monitoring system that analyses body posture and movement. Supervised machine learning strategies applied to noncontact vision-based infrared camera data using a transfer learning approach, successfully quantified sleep poses of participants covered by a blanket. This represents the first occasion that such a machine learning approach has been used to successfully detect four predefined poses and the empty bed state during 8-10 hour overnight sleep episodes representing a realistic domestic sleep situation. The methodology was evaluated against manually scored sleep poses and poses estimated using clinical polysomnography measurement technology. In a cohort of 12 healthy participants, we find that a ResNet-152 pre-trained network achieved the best performance compared with the standard de novo CNN network and other pre-trained networks. The performance of our approach was better than other video-based methods for sleep pose estimation and produced higher performance compared to the clinical standard for pose estimation using a polysomnography position sensor. It can be concluded that infrared video capture coupled with deep learning AI can be successfully used to quantify sleep poses as well as the transitions between poses in realistic nocturnal conditions, and that this non-contact approach provides superior pose estimation compared to currently accepted clinical methods.

中文翻译：

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使用单个 2D 红外摄像头进行临床睡眠姿势检测的迁移学习


睡眠质量是人类健康和福祉的重要决定因素。需要能够大规模量化睡眠质量的新技术来诊断睡眠不良并进行流行病学研究。睡眠质量的一项重要指标是身体姿势。在本文中，我们介绍了一种分析身体姿势和运动的非接触式睡眠监测系统的设计和实现。使用迁移学习方法将监督机器学习策略应用于基于非接触视觉的红外摄像机数据，成功量化了被毯子覆盖的参与者的睡眠姿势。这是首次使用这种机器学习方法成功检测代表现实家庭睡眠情况的 8-10 小时夜间睡眠期间的四种预定义姿势和空床状态。该方法根据手动评分的睡眠姿势和使用临床多导睡眠图测量技术估计的姿势进行评估。在 12 名健康参与者的队列中，我们发现与标准 de novo CNN 网络和其他预训练网络相比，ResNet-152 预训练网络取得了最佳性能。我们的方法的性能优于其他基于视频的睡眠姿势估计方法，并且与使用多导睡眠图位置传感器进行姿势估计的临床标准相比，产生了更高的性能。可以得出的结论是，红外视频捕获与深度学习人工智能相结合，可以成功地用于量化睡眠姿势以及现实夜间条件下姿势之间的转换，并且与目前接受的临床方法相比，这种非接触式方法提供了更好的姿势估计。