PDF(Pubmed)
Abstract：
Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual\'s sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars-three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors.
摘要：
评估睡眠姿势，睡眠测试的关键组成部分，对于了解个人的睡眠质量和识别潜在的睡眠障碍至关重要。然而,传统上，由于诸如弱光条件和毯子之类的障碍物等因素，监测睡眠姿势提出了重大挑战。雷达技术的使用可能是一个潜在的解决方案。这项研究的目的是确定雷达传感器的最佳数量和位置，以实现准确的睡眠姿势估计。我们邀请70名参与者在不同厚度的毯子下采取9种不同的睡眠姿势。这是在配备有八个雷达基线的环境中进行的，其中三个位于床头板，五个位于侧面。我们提出了一种生成雷达地图的新技术，空间无线电回波图(SREM)专为跨多个雷达的数据融合而设计。使用多视图卷积神经网络(MVCNN)进行睡眠姿势估计，作为各种深度特征提取器比较评估的总体框架，包括ResNet-50、EfficientNet-50、DenseNet-121、PHResNet-50、Attention-50和SwinTransformer。其中,DenseNet-121达到了最高的精度，九级粗、四级细粒度分类得分为0.534分和0.804分，分别。这导致了对雷达最佳集合的进一步分析。对于位于头部的雷达，一个位于左侧的雷达被证明既必要又足够，达到0.809的精度。当只使用一个中央头颅雷达时，省略中央侧雷达并仅保留三个上身雷达的精度分别为0.779和0.753。这项研究为确定该应用中的最佳传感器配置奠定了基础，同时还探索了精度和使用更少的传感器之间的权衡。
