Integrating Health Sensing into Cellular Networks: Human Sleep Monitoring Using 5G Signals

TL;DR

This study demonstrates the feasibility of using commercial 5G signals for device-free human sleep monitoring, achieving high accuracy in respiration and movement detection through a novel signal processing and machine learning approach.

Contribution

First experimental validation of sleep monitoring using real 5G signals from commercial infrastructure with a new processing pipeline and CNN-based classification.

Findings

Over 91.2% accuracy in respiration rate estimation

85.5% accuracy in sleep movement classification

Effective use of uplink CSI for health monitoring

Abstract

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.