Noncontact Multi-person Respiration Detection Using Time Modulated Array

Radio frequency (RF) sensing has been commonly employed in health monitoring because of its advantages of noncontact, wide coverage, and privacy protection. Respiratory monitoring within multi-person environments has emerged as a prominent research area and poses significant challenges in the field of RF sensing. Current approaches to multi-person respiratory monitoring primarily depend on the inherent resolution of sensing devices, such as range and angular resolution. To address this limitation, this paper proposes a novel single-channel method for multi-person respiration detection based on a time-modulated array (TMA). The TMA is employed to generate multiple harmonic beams, enabling the simultaneous detection of human respiration from multiple directions. Each harmonic component is extracted through a series of digital down-conversion (DDC) operations applied to the received single-channel signal. The respiratory signal of each individual is then obtained by analyzing the phase information of the corresponding harmonic component. A multi-person respiratory monitoring system was designed and implemented using a 2.4 GHz TMA continuous-wave radar prototype. The system features a simple architecture and low computational complexity. In experiments of single-person, two-person, and three-person scenarios, the maximum RR estimation errors are 0.59, 0.98, and 1.30 bpm, respectively. The proposed system can accurately detect the respiratory rates of three individuals simultaneously and effectively identify abnormal breathing patterns among them, demonstrating its capability to monitor health status in daily multi-person scenarios.

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Publication Details

Journal
IEEE Sensors Journal
Published
2025-01-01
DOI
https://doi.org/10.1109/jsen.2025.3589182
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
0.00

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article

Noncontact Multi-person Respiration Detection Using Time Modulated Array

Dou Fan, Junhuai Li, Levent Sevgi, Lei Guan et al.
3 links
IEEE Sensors Journal
Advanced Chemical Sensor Technologies
article

Noncontact Multi-person Respiration Detection Using Time Modulated Array

Dou Fan, Junhuai Li, Levent Sevgi, Lei Guan, Tao Huang, Rong Fei
article en

Abstract

Radio frequency (RF) sensing has been commonly employed in health monitoring because of its advantages of noncontact, wide coverage, and privacy protection. Respiratory monitoring within multi-person environments has emerged as a prominent research area and poses significant challenges in the field of RF sensing. Current approaches to multi-person respiratory monitoring primarily depend on the inherent resolution of sensing devices, such as range and angular resolution. To address this limitation, this paper proposes a novel single-channel method for multi-person respiration detection based on a time-modulated array (TMA). The TMA is employed to generate multiple harmonic beams, enabling the simultaneous detection of human respiration from multiple directions. Each harmonic component is extracted through a series of digital down-conversion (DDC) operations applied to the received single-channel signal. The respiratory signal of each individual is then obtained by analyzing the phase information of the corresponding harmonic component. A multi-person respiratory monitoring system was designed and implemented using a 2.4 GHz TMA continuous-wave radar prototype. The system features a simple architecture and low computational complexity. In experiments of single-person, two-person, and three-person scenarios, the maximum RR estimation errors are 0.59, 0.98, and 1.30 bpm, respectively. The proposed system can accurately detect the respiratory rates of three individuals simultaneously and effectively identify abnormal breathing patterns among them, demonstrating its capability to monitor health status in daily multi-person scenarios.

IEEE Sensors Journal
PLA Electronic Engineering Institute (CN), Istanbul Technical University (TR), James Cook University (AU)
Natural Science Basic Research Program of Shaanxi Province
Openalex Percentile: Top 83%
Advanced Chemical Sensor Technologies
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