Shenzhen lnstitute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
University of Chinese Academy of Sciences, Beijing 100190, China.
Sensors (Basel). 2024 Sep 11;24(18):5900. doi: 10.3390/s24185900.
Recognizing sleep posture is crucial for the monitoring of people with sleeping disorders. Existing contact-based systems might interfere with sleeping, while camera-based systems may raise privacy concerns. In contrast, radar-based sensors offer a promising solution with high penetration ability and the capability to detect vital bio-signals. This study propose a deep learning method for human sleep pose recognition from signals acquired from single-antenna Frequency-Modulated Continuous Wave (FMCW) radar device. To capture both frequency features and sequential features, we introduce ResTCN, an effective architecture combining Residual blocks and Temporal Convolution Network (TCN) to recognize different sleeping postures, from augmented statistical motion features of the radar time series. We rigorously evaluated our method with an experimentally acquired data set which contains sleeping radar sequences from 16 volunteers. We report a classification accuracy of 82.74% on average, which outperforms the state-of-the-art methods.
识别睡眠姿势对于监测睡眠障碍患者至关重要。现有的基于接触的系统可能会干扰睡眠，而基于摄像头的系统可能会引发隐私问题。相比之下，基于雷达的传感器具有高穿透能力和检测重要生物信号的能力，是一种很有前途的解决方案。本研究提出了一种基于单天线调频连续波(FMCW)雷达设备获取的信号的人体睡眠姿势识别深度学习方法。为了同时捕获频率特征和序列特征，我们引入了 ResTCN，这是一种有效的结合残差块和时间卷积网络(TCN)的架构，用于从雷达时间序列的增强统计运动特征中识别不同的睡眠姿势。我们使用一个实验采集的数据集对我们的方法进行了严格的评估，该数据集包含 16 个志愿者的睡眠雷达序列。我们报告的平均分类准确率为 82.74%，优于现有的方法。
