IEEE Trans Biomed Circuits Syst. 2007 Sep;1(3):217-27. doi: 10.1109/TBCAS.2007.914481.
Sleep monitoring is an important issue and has drawn considerable attention in medicine and healthcare. Given that traditional approaches, such as polysomnography, are usually costly, and often require subjects to stay overnight at clinics, there has been a need for a low-cost system suitable for long-term sleep monitoring. In this paper, we propose a system using low-cost multimodality sensors such as video, passive infrared, and heart-rate sensors for sleep monitoring. We apply machine learning methods to automatically infer a person's sleep state, especially differentiating sleep and wake states. This is useful information for inferring sleep latency, efficiency, and duration that are important for long-term monitoring of sleep quality in healthy individuals and in those with a sleep-related disorder diagnosis. Our experiments show that the proposed approach offers reasonable performance compared to an existing standard approach (i.e., actigraphy), and that multimodality data fusion can improve the robustness and accuracy of sleep state detection.
睡眠监测是一个重要的问题，在医学和医疗保健领域引起了相当大的关注。鉴于传统方法（如多导睡眠图）通常成本较高，并且通常需要受试者在诊所过夜，因此需要一种适合长期睡眠监测的低成本系统。在本文中，我们提出了一种使用低成本多模态传感器（如视频、被动红外和心率传感器）进行睡眠监测的系统。我们应用机器学习方法自动推断一个人的睡眠状态，特别是区分睡眠和清醒状态。这对于推断睡眠潜伏期、效率和持续时间非常有用，这些对于长期监测健康个体和睡眠相关障碍诊断个体的睡眠质量非常重要。我们的实验表明，与现有的标准方法（即活动记录仪）相比，所提出的方法具有合理的性能，并且多模态数据融合可以提高睡眠状态检测的鲁棒性和准确性。
