Integrating Health Sensing into Cellular Networks: Human Sleep Monitoring Using 5G Signals
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914364880060416 |
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| author | Lin, Ruxin Yan, Peihao Lu, Jie Wang, Qijun Zeng, Huacheng |
| author_facet | Lin, Ruxin Yan, Peihao Lu, Jie Wang, Qijun Zeng, Huacheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02558 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Integrating Health Sensing into Cellular Networks: Human Sleep Monitoring Using 5G Signals Lin, Ruxin Yan, Peihao Lu, Jie Wang, Qijun Zeng, Huacheng Networking and Internet Architecture 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. |
| title | Integrating Health Sensing into Cellular Networks: Human Sleep Monitoring Using 5G Signals |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2603.02558 |