High-Fidelity and Location-Robust Respiratory Waveform Monitoring with Single-Antenna WiFi
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915948821217280 |
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| author | Wang, Hefei Liu, Jianwei He, Yinghui Yu, Guanding Han, Jinsong |
| author_facet | Wang, Hefei Liu, Jianwei He, Yinghui Yu, Guanding Han, Jinsong |
| contents | In recent years, WiFi sensing has been recognized as a promising technology to bring respiratory monitoring into everyday homes, thanks to its contactless nature and ubiquitous availability. However, existing WiFi-based respiratory monitoring systems still fall short of deployment-oriented performance: they suffer from restrained hardware scalability, limited accuracy, and are highly sensitive to user location. To overcome these limitations and push WiFi sensing towards clinically meaningful precision, we propose RespirFi, a novel system that robustly delivers high-fidelity respiratory waveforms with WiFi Channel State Information (CSI), thereby enabling accurate estimation of key physiological biomarkers. At the core of RespirFi is a theoretical human reflection model, through which we perform an in-depth characterization of how CSI variations are shaped by both subcarrier frequency and spatial user location. Guided by these insights, we develop a location-robust waveform construction method that adaptively selects high quality subcarriers and aligns their waveform trends, ensuring accurate waveform recovery. Furthermore, we propose a breathing phase identification method that leverages inter-subcarrier CSI differences to reliably distinguish inhalation from exhalation. We implement RespirFi over commodity WiFi devices, and extensive experiments demonstrate that it outperforms state-of-the-art approaches across a wide range of clinically relevant respiratory metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20397 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | High-Fidelity and Location-Robust Respiratory Waveform Monitoring with Single-Antenna WiFi Wang, Hefei Liu, Jianwei He, Yinghui Yu, Guanding Han, Jinsong Signal Processing In recent years, WiFi sensing has been recognized as a promising technology to bring respiratory monitoring into everyday homes, thanks to its contactless nature and ubiquitous availability. However, existing WiFi-based respiratory monitoring systems still fall short of deployment-oriented performance: they suffer from restrained hardware scalability, limited accuracy, and are highly sensitive to user location. To overcome these limitations and push WiFi sensing towards clinically meaningful precision, we propose RespirFi, a novel system that robustly delivers high-fidelity respiratory waveforms with WiFi Channel State Information (CSI), thereby enabling accurate estimation of key physiological biomarkers. At the core of RespirFi is a theoretical human reflection model, through which we perform an in-depth characterization of how CSI variations are shaped by both subcarrier frequency and spatial user location. Guided by these insights, we develop a location-robust waveform construction method that adaptively selects high quality subcarriers and aligns their waveform trends, ensuring accurate waveform recovery. Furthermore, we propose a breathing phase identification method that leverages inter-subcarrier CSI differences to reliably distinguish inhalation from exhalation. We implement RespirFi over commodity WiFi devices, and extensive experiments demonstrate that it outperforms state-of-the-art approaches across a wide range of clinically relevant respiratory metrics. |
| title | High-Fidelity and Location-Robust Respiratory Waveform Monitoring with Single-Antenna WiFi |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.20397 |