Range Expansion Method for WiFi-Based Respiration Monitoring Under Dynamic Scenes

Lin Yiqun; Qiu Jiefan; Zhang Jinhong; Zhou Kezhong; Fang Kai; Liu Xiaoying; Chi Kaikai

doi:10.7544/issn1000-1239.202330884

Journal of Computer Research and Development > 2025 > 62(4): 1075-1089. > DOI: 10.7544/issn1000-1239.202330884 CSTR: 32373.14.issn1000-1239.202330884

Lin Yiqun, Qiu Jiefan, Zhang Jinhong, Zhou Kezhong, Fang Kai, Liu Xiaoying, Chi Kaikai. Range Expansion Method for WiFi-Based Respiration Monitoring Under Dynamic Scenes[J]. Journal of Computer Research and Development, 2025, 62(4): 1075-1089. DOI: 10.7544/issn1000-1239.202330884

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Range Expansion Method for WiFi-Based Respiration Monitoring Under Dynamic Scenes

1.
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023
2.
College of Mathematics and Computer Science, Zhejiang Agricultural and Forestry University, Hangzhou 311300

Funds: This work was supported by the National Natural Science Foundation of China (61872322, 62372412) and the Zhejiang Provincial Natural Science Foundation (LY20F020026).

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Author Bio:
Lin Yiqun: born in 1999. Master. Member of CCF. His main research interests include wireless sensing and artificial intelligence

Qiu Jiefan: born in 1984. PhD, associate professor. Member of CCF. His main research interests include embedded operation system, IoT, and artificial intelligence

Zhang Jinhong: born in 2002. Bachelor. Member of CCF. His main research interests include IoT and artificial intelligence

Zhou Kezhong: born in 2000. Master. Member of CCF. His main research interests include IoT and wireless sensing

Fang Kai: born in 1992. PhD, professor. His main research interests include IoT, network security, deep learning, and artificial intelligence

Liu Xiaoying: born in 1990. PhD, associate professor. Senior member of CCF. Her main research interests include intelligent IoT, energy harvesting wireless networks and age of information

Chi Kaikai: born in 1980. PhD, professor. Member of CCF. His main research interests include wireless powered communication networks and edge computing
Received Date: October 31, 2023
Revised Date: May 15, 2024
Accepted Date: August 08, 2024
Available Online: August 13, 2024

Graphical Abstract

Abstract

Abstract

WiFi-based respiratory monitoring becomes a hot spot in the sensing layer of IoT benefiting from non-contact, low cost and high privacy protection. However, current WiFi-based respiratory monitoring methods relay on sensitive channel state information (CSI) samples which requires that single monitoring target keeps static without any moving non-target person and closing to the WiFi transceiver device. These requirements limit the large-scale applications of WiFi-based respiratory monitoring. Therefore, we propose a respiratory monitoring range extension method named FDRadio, which is able to work under dynamic interference scenes. In FDRadio, we improve the accuracy and robustness of respiratory monitoring from three aspects: separating dynamic interference sources, eliminating ambient noise and enhancing power of dynamic reflected signal. Specifically, we first expand the channel bandwidth by combining multiple WiFi channels to improve the spatial resolution of WiFi sensing, and employ wired direct channel to remove the accumulated hardware noise caused by combining channels. Second, we analyze the relationship between monitoring range and ambient noise, and then adopt time diversity techniques to design a two-stage ambient noise deduction process for FDRadio. In addition, we design a novel weight allocation algorithm, which maximizes the dynamic reflected signal power, and enhances the ability to sensing weak chest fluctuation caused by breath. Finally, the processed CSI samples are converted to power delay spectrum (PDP) in time domain. By this, the respiratory signal can be directly extracted from the target person using the distance difference. We implement FDRadio on a commercial embedded devices and conduct a series of experiments. The experimental results show that detection error is less than 0.5 bpm under the 7m available monitoring range, even if multiple moving non-target person exists.
- Internet of things,
- WiFi sensing,
- channel state information,
- dynamic scene,
- respiration rate monitoring,
- signal diversity

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References (49)

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