Cellular networks offer a unique opportunity to enable device-free and wide-area health monitoring by exploiting the sensitivity of radio-frequency (RF) propagation to human physiological activities. In this paper, we present the first experimental study of human sleep monitoring using realistic 5G signals collected from commercial cellular infrastructure. We investigate a practical scenario in which a smartphone is placed near a bed, and a 5G base station periodically configures uplink sounding reference signal (SRS) transmissions to obtain fine-grained channel state information (CSI). Leveraging uplink CSI measurements, we design a lightweight signal processing pipeline for respiration rate estimation and a CNN model for sleep body movement classification. Through extensive experiments conducted on an indoor private 5G network, our system achieves over 91.2% accuracy in respiration rate estimation and 85.5% accuracy in sleep movement classification.

翻译：蜂窝网络通过利用射频传播对人体生理活动的敏感性，为实现无设备、广域健康监测提供了独特机遇。本文首次利用从商用蜂窝基础设施采集的真实5G信号，开展了人体睡眠监测的实验研究。我们探讨了一种实用场景：将智能手机置于床边，5G基站周期性配置上行探测参考信号传输以获取细粒度信道状态信息。基于上行CSI测量，我们设计了用于呼吸频率估计的轻量级信号处理流程，以及用于睡眠体动分类的CNN模型。通过在室内私有5G网络上进行大量实验，我们的系统在呼吸频率估计方面达到91.2%以上的准确率，在睡眠体动分类方面达到85.5%的准确率。