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

2026-03-02

AI Summary

This work proposes a device-free, wide-area sleep health monitoring approach leveraging commercial 5G networks. By exploiting the uplink Sounding Reference Signal (SRS) from 5G base stations to obtain high-precision Channel State Information (CSI), the method employs a lightweight signal processing pipeline to estimate respiratory rate and utilizes a convolutional neural network (CNN) to classify sleep-related body movements. To the best of our knowledge, this is the first demonstration of non-intrusive sleep monitoring in a real-world commercial 5G deployment without requiring users to wear any devices, offering both practicality and scalability. Experimental results in an indoor private 5G network show a respiratory rate estimation accuracy of 91.2% and a sleep movement classification accuracy of 85.5%.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

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.

Problem

• health monitoring
• sleep monitoring
• 5G signals
• device-free sensing
• cellular networks

Innovation

• 5G signals
• device-free health monitoring
• channel state information (CSI)
• respiration rate estimation
• sleep movement classification