Radar-based Monitoring of Vital Signs: A Tutorial Overview

Paterniani, Giacomo; Sgreccia, Daria; DAVOLI, ALESSANDRO; Guerzoni, Giorgio; Viesti, Pasquale Di; Valenti, Anna Chiara; Vitolo, Marco; Vitetta, G.M.; Boriani, Giuseppe

doi:10.36227/techrxiv.19212918

Cited by 4 publications

(2 citation statements)

References 94 publications

(234 reference statements)

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“…The task of extracting vital signs from radar data requires complex signal processing. Since the scope of this paper is a hardware characterization, a previously published pipeline, which is conducive to low-power systems, for HR and RR estimation has been used [22] and illustrated in Figure 3.…”

Section: B Signal Processingmentioning

confidence: 99%

“…The displacement signal is band-pass filtered for the heart beat signal between 0.7 Hz-2 Hz and for the respiratory signal between 0.1 Hz-0.5 Hz. These frequency bands correspond to 42-120 bpm and 6-30 breaths per minute, which is a reasonable range for these vital signs [22]. For the purpose of this system characterisation, the heart and respiration rates are obtained from the frequency with the peak amplitude in the FFT of the band-pass filtered signals.…”

Section: B Signal Processingmentioning

confidence: 99%

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Investigation of mmWave Radar Technology For Non-contact Vital Sign Monitoring

Marty,

Pantanella,

Ronco

et al. 2023

2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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Non-contact vital sign monitoring has many advantages over conventional methods in being comfortable, unobtrusive and without any risk of spreading infection. The use of millimeter-wave (mmWave) radars is one of the most promising approaches that enable contact-less monitoring of vital signs. Novel low-power implementations of this technology promise to enable vital sign sensing in embedded, battery-operated devices. The nature of these new low-power sensors exacerbates the challenges of accurate and robust vital sign monitoring and especially the problem of heart-rate tracking. This work focuses on the investigation and characterization of three Frequency Modulated Continuous Wave (FMCW) low-power radars with different carrier frequencies of 24 GHz, 60 GHz and 120 GHz. The evaluation platforms were first tested on phantom models that emulated human bodies to accurately evaluate the baseline noise, error in range estimation, and error in displacement estimation. Additionally, the systems were also used to collect data from three human subjects to gauge the feasibility of identifying heartbeat peaks and breathing peaks with simple and lightweight algorithms that could potentially run in low-power embedded processors. The investigation revealed that the 24 GHz radar has the highest baseline noise level, 0.04 mm at 0°angle of incidence, and an error in range estimation of 3.45 ± 1.88 cm at a distance of 60 cm. At the same distance, the 60 GHz and the 120 GHz radar system shows the least noise level, 0.01 mm at 0°angle of incidence, and error in range estimation 0.64 ± 0.01 cm and 0.04 ± 0.0 cm respectively. Additionally, tests on humans showed that all three radar systems were able to identify heart and breathing activity but the 120 GHz radar system outperformed the other two.

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Section: B Signal Processingmentioning

confidence: 99%

Section: B Signal Processingmentioning

confidence: 99%

Investigation of mmWave Radar Technology For Non-contact Vital Sign Monitoring

Marty,

Pantanella,

Ronco

et al. 2023

2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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Poster Abstract: Contactless Vital Sign Monitoring using Low-Power FMCW Radar

Marty

Dheman

Magno

2023

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation

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IR-UWB Radar-Based Robust Heart Rate Detection Using a Deep Learning Technique Intended for Vehicular Applications

et al. 2022

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This paper deals with robust heart rate detection intended for the in-car monitoring of people. There are two main problems associated with radar-based heart rate detection. Firstly, the signal associated with the human heart is difficult to separate from breathing harmonics in the frequency domain. Secondly, the vital signal is affected by any interference signal from hand gestures, lips motion during speech or any other random body motions (RBM). To handle the problem of the breathing harmonics, we propose a novel algorithm based on time series data instead of the conventionally used frequency domain technique. In our proposed method, a deep learning classifier is used to detect the pattern of the heart rate signal. To deal with the interference mitigation from the random body motions, we identify an optimum location for the radar sensor inside the car. In this paper, a commercially available Novelda Xethru X4 radar is used for signal acquisition and vital sign measurement of 5 people. The performance of the proposed algorithm is compared with and found to be superior to that of the conventional frequency domain technique.

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Radar-based Monitoring of Vital Signs: A Tutorial Overview

Abstract: Overview of radar-based methods for vital sign monitoring

Cited by 4 publications

References 94 publications

Investigation of mmWave Radar Technology For Non-contact Vital Sign Monitoring

Investigation of mmWave Radar Technology For Non-contact Vital Sign Monitoring

Poster Abstract: Contactless Vital Sign Monitoring using Low-Power FMCW Radar

IR-UWB Radar-Based Robust Heart Rate Detection Using a Deep Learning Technique Intended for Vehicular Applications

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