Analysis of Wireless Vital Sign Detection Method
Research Article
Open Access
CC BY

Analysis of Wireless Vital Sign Detection Method

Haoxuan Xu 1*
1 Yangzheng Middle School, Jinjiang City, Quanzhou City, 362000, China
*Corresponding author: xuhaoxuan0105@outlook.com
Published on 10 July 2025
Journal Cover
ACE Vol.174
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-235-5
ISBN (Online): 978-1-80590-236-2
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Abstract

As technology advances and living standards improve, people's awareness of vital signs monitoring is gradually increasing. Meanwhile, the potential of wireless sensing technology in this field has become increasingly evident. Wireless sensing technology can efficiently monitor the health status of individuals by analyzing changes in wireless signals. This article highlights four typical wireless sensing vital signs monitoring technologies. By employing methods such as Fresnel zone modeling, intelligent reflective surface (IRS) optimization, and a multi-module system framework, these technologies are applied practically. The article concludes that the RSSI model has high accuracy for single-person detection in fixed scenarios but is sensitive to the environment and location. The CSI combined with the IRS model enhances robustness through dynamic beamforming, and improves multi-user separation accuracy, but comes with higher hardware costs. The article aims to deepen the understanding of wireless sensing vital signs monitoring and to provide theoretical support and technical analysis for the advancement of wireless sensing in this field.

Keywords:

Wireless sensing, WiFi signal, breathing detection, heart rate detection.

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Xu,H. (2025). Analysis of Wireless Vital Sign Detection Method. Applied and Computational Engineering,174,39-45.

References

[1]. Guo Zhengxin. Study on a WiFi-based Non-contact Personnel Activity Perception Method [D]. Nanjing University of Posts and Telecommunications, 2022.

[2]. Yuan Wenyang. Research on the Detection Boundary of Human Activity Based on WiFi Channel State Information [D]. Nanjing University of Posts and Telecommunications, 2023.

[3]. Yang Yilong, Liang Chenhua, Feng Yifei, et al. Research Status and Prospects of Non-contact Vital Sign Monitoring Technology [J]. Chinese Medical Equipment, 2023, 38(06): 151-156.

[4]. Wang Dachun, Li Guohe, Wang Feng, et al. Research status of tool wear monitoring based on deep learning [J]. Tool Technology, 2022, 56(06): 3-13.

[5]. Zhang Yixing. Research and System Implementation of a Defect Detection Method for Ampoules Based on Deep Learning [D]. Shijiazhuang Tiedao University, 2021.

[6]. Chen Tianqi, Zhang Yuqian, Zong Baochao, et al. Development and application of heart rate and respiratory life characteristic monitoring technology [J]. Chinese Journal of Medical Devices, 2021, 45(02): 188-193.

[7]. Yu Yiran. Research on non-contact respiratory detection in WiFi scenarios [D]. Yunnan University, 2020.

[8]. Cui Min. Data Processing of Electrical Equipment Monitoring Based on Text Recognition Technology [D]. North China Electric Power University, 2019.

[9]. Tong Tingyang and Ma Zhenzhou. Near-field detection technology for the consistency of ultra-high frequency RFID tags [J]. Electronic Technology Application, 2013, 39(04): 62-64.

[10]. Yue Yu. Research on Separation Technology of Heart and Respiratory Signals in Biological Radar Detection Technology [D]. Fourth Military Medical University, 2007.

[11]. Yi Yeqing. Blind Source Signal Separation Based on Evolutionary Algorithm [D]. Hunan University, 2005.

References

[1]. Guo Zhengxin. Study on a WiFi-based Non-contact Personnel Activity Perception Method [D]. Nanjing University of Posts and Telecommunications, 2022.

[2]. Yuan Wenyang. Research on the Detection Boundary of Human Activity Based on WiFi Channel State Information [D]. Nanjing University of Posts and Telecommunications, 2023.

[3]. Yang Yilong, Liang Chenhua, Feng Yifei, et al. Research Status and Prospects of Non-contact Vital Sign Monitoring Technology [J]. Chinese Medical Equipment, 2023, 38(06): 151-156.

[4]. Wang Dachun, Li Guohe, Wang Feng, et al. Research status of tool wear monitoring based on deep learning [J]. Tool Technology, 2022, 56(06): 3-13.

[5]. Zhang Yixing. Research and System Implementation of a Defect Detection Method for Ampoules Based on Deep Learning [D]. Shijiazhuang Tiedao University, 2021.

[6]. Chen Tianqi, Zhang Yuqian, Zong Baochao, et al. Development and application of heart rate and respiratory life characteristic monitoring technology [J]. Chinese Journal of Medical Devices, 2021, 45(02): 188-193.

[7]. Yu Yiran. Research on non-contact respiratory detection in WiFi scenarios [D]. Yunnan University, 2020.

[8]. Cui Min. Data Processing of Electrical Equipment Monitoring Based on Text Recognition Technology [D]. North China Electric Power University, 2019.

[9]. Tong Tingyang and Ma Zhenzhou. Near-field detection technology for the consistency of ultra-high frequency RFID tags [J]. Electronic Technology Application, 2013, 39(04): 62-64.

[10]. Yue Yu. Research on Separation Technology of Heart and Respiratory Signals in Biological Radar Detection Technology [D]. Fourth Military Medical University, 2007.

[11]. Yi Yeqing. Blind Source Signal Separation Based on Evolutionary Algorithm [D]. Hunan University, 2005.

Cite this article

Xu,H. (2025). Analysis of Wireless Vital Sign Detection Method. Applied and Computational Engineering,174,39-45.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-CDS 2025 Symposium: Data Visualization Methods for Evaluatio

ISBN: 978-1-80590-235-5(Print) / 978-1-80590-236-2(Online)
Editor: Marwan Omar, Elisavet Andrikopoulou
Conference website: https://2025.confcds.org/portsmouth.html
Conference date: 30 July 2025
Series: Applied and Computational Engineering
Volume number: Vol.174
ISSN: 2755-2721(Print) / 2755-273X(Online)

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