Software-Defined Doppler Radar Sensor for Human Breathing Detection

Costanzo, Sandra

doi:10.3390/s19143085

Cited by 27 publications

(8 citation statements)

References 16 publications

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“…Radar sensors are used as a non-contact and wireless method of breathing and movement detection [ 145 ]. These signals can then be used for sleep staging.…”

Section: Resultsmentioning

confidence: 99%

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Imtiaz

2021

Sensors

107

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Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.

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“…Radar sensors are used as a non-contact and wireless method of breathing and movement detection [ 145 ]. These signals can then be used for sleep staging.…”

Section: Resultsmentioning

confidence: 99%

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Imtiaz

2021

Sensors

107

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“…In order to extract phase information, the complex signal demodulation (CSD) method [30,31] is used to process quadrature channel baseband signals as:…”

Section: Radar Detection Theorymentioning

confidence: 99%

Non-Contact Speech Recovery Technology Using a 24 GHz Portable Auditory Radar and Webcam

Hong

et al. 2020

Remote Sensing

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Language has been one of the most effective ways of human communication and information exchange. To solve the problem of non-contact robust speech recognition, recovery, and surveillance, this paper presents a speech recovery technology based on a 24 GHz portable auditory radar and webcam. The continuous-wave auditory radar is utilized to extract the vocal vibration signal, and the webcam is used to obtain the fitted formant frequency. The traditional formant speech synthesizer is selected to synthesize and recover speech, using the vocal vibration signal as the sound source excitation and the fitted formant frequency as the vocal tract resonance characteristics. Experiments on reading single English characters and words are carried out. Using microphone records as a reference, the effectiveness of the proposed speech recovery technology is verified. Mean opinion scores show a relatively high consistency between the synthesized speech and original acoustic speech.

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“…Finally, referring to the methods based on modulation of other physiological signals, the well-known ECG-derived respiration (EDR) [57,58] measures ECG morphology changes due to sinus arrhythmia, relative movement of heart and electrodes and changes in lung volume, whereas the photoplethysmography (PPG)-derived respiration exploits the modulation in amplitude, baseline and frequency of the PPG signal caused by changes in blood stroke volume, heart rate and tissue blood volume and the variation of its pulse wave width under changes in artery stiffness during the respiratory activity [59,60]. On the other hand, contactless techniques comprise methods based on environmental respiratory sounds (e.g., microphones), air temperature (e.g., thermal cameras), chest wall movements (e.g., radar sensors, marker-based stereophotogrammetric systems, stereoscopic camera sensors) or modulation of other physiological signals (e.g., light intensity measurement, RGB cameras) [8,[61][62][63][64][65][66][67][68][69][70][71][72][73][74]. The great part of the instrumentation required by such methods is generally cumbersome and far from being wearable, or even portable, so its use has remained confined to research or clinical settings [1,2].…”

Section: Introductionmentioning

confidence: 99%

Respiration Monitoring via Forcecardiography Sensors

Andreozzi

Centracchio

Punzo

et al. 2021

Sensors

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In the last few decades, a number of wearable systems for respiration monitoring that help to significantly reduce patients’ discomfort and improve the reliability of measurements have been presented. A recent research trend in biosignal acquisition is focusing on the development of monolithic sensors for monitoring multiple vital signs, which could improve the simultaneous recording of different physiological data. This study presents a performance analysis of respiration monitoring performed via forcecardiography (FCG) sensors, as compared to ECG-derived respiration (EDR) and electroresistive respiration band (ERB), which was assumed as the reference. FCG is a novel technique that records the cardiac-induced vibrations of the chest wall via specific force sensors, which provide seismocardiogram-like information, along with a novel component that seems to be related to the ventricular volume variations. Simultaneous acquisitions were obtained from seven healthy subjects at rest, during both quiet breathing and forced respiration at higher and lower rates. The raw FCG sensor signals featured a large, low-frequency, respiratory component (R-FCG), in addition to the common FCG signal. Statistical analyses of R-FCG, EDR and ERB signals showed that FCG sensors ensure a more sensitive and precise detection of respiratory acts than EDR (sensitivity: 100% vs. 95.8%, positive predictive value: 98.9% vs. 92.5%), as well as a superior accuracy and precision in interbreath interval measurement (linear regression slopes and intercepts: 0.99, 0.026 s (R2 = 0.98) vs. 0.98, 0.11 s (R2 = 0.88), Bland–Altman limits of agreement: ±0.61 s vs. ±1.5 s). This study represents a first proof of concept for the simultaneous recording of respiration signals and forcecardiograms with a single, local, small, unobtrusive, cheap sensor. This would extend the scope of FCG to monitoring multiple vital signs, as well as to the analysis of cardiorespiratory interactions, also paving the way for the continuous, long-term monitoring of patients with heart and pulmonary diseases.

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Software-Defined Doppler Radar Sensor for Human Breathing Detection

Cited by 27 publications

References 16 publications

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Non-Contact Speech Recovery Technology Using a 24 GHz Portable Auditory Radar and Webcam

Respiration Monitoring via Forcecardiography Sensors

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