Respiratory Rate Sensing for a Non-Stationary Human Assisted by Motion Detection

Non-contact human respiration rate monitoring can be used for sleep apnea detection and home care. Typically, the human body does not remain stationary for long periods, and body movement can significantly affect the performance of non-contact respiratory monitoring. Because the breathing rate gener...

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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 25; no. 7; p. 2267
Main Authors Hsu, Hsi-Chou, Chen, Wei-Hsin, Lin, Yi-Wen, Huang, Yung-Fa
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 03.04.2025
MDPI
Subjects
Algorithms
Antennas
Comparative analysis
Diagnosis
Doppler effect
Fourier Analysis
Fourier transformations
Fourier transforms
Human mechanics
Humans
Measurement
Monitoring, Physiologic - methods
Motion
Movement - physiology
non-stationary
Physiological aspects
Radar
radar signal processing
Respiration
respiratory rate
Respiratory Rate - physiology
Signal processing
Signal Processing, Computer-Assisted
Sleep apnea syndromes
ultra-wideband (UWB) radar
Velocity
Wavelet transforms
Taiwan
ultra-wideband (UWB) radar
non-stationary
respiratory rate
radar signal processing
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Summary:Non-contact human respiration rate monitoring can be used for sleep apnea detection and home care. Typically, the human body does not remain stationary for long periods, and body movement can significantly affect the performance of non-contact respiratory monitoring. Because the breathing rate generally remains stable over short periods, using measurements from only a portion of the radar echo signals does not result in significant errors, and these errors will be smaller than those caused by body movement. However, selecting a window size that is too short reduces frequency resolution, leading to increased estimation errors. Choosing an appropriate window length can improve estimation accuracy. In this paper, we propose an algorithm to determine whether the subject is stationary and select the received signal with minimal body movement. Experimental results are compared using alternative schemes, including fast Fourier transform (FFT), short-time Fourier transform (STFT), and RGB-D camera-assisted methods, in terms of root mean square error (RMSE) performance.
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This paper is an extended version of our published paper: Hsu, H.-C.; Lin, Y.-W.; Huang, Y.-F. Remote Monitoring of Respiratory Rate for a Non-Stationary Human Using Ultra Wideband Radars. In Proceedings of the 2023 12th International Conference on Awareness Science and Technology (iCAST), Taichung, Taiwan, 9–11 November 2023.
ISSN:1424-8220
1424-8220
DOI:10.3390/s25072267