Multitarget Vital Signs Measurement With Chest Motion Imaging Based on MIMO Radar

Feng, Chen; Jiang, Xiaonan; Jeong, Min‐Gyo; Hong, Hong; Fu, Changhong; Yang, Xiaohui; Wang, E.; Zhu, Xiaohua; Liu, Xiaoguang

doi:10.1109/tmtt.2021.3076239

Cited by 37 publications

(17 citation statements)

References 41 publications

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“…4D imaging and multiple input multiple output (MIMO) beamforming radars could be used for this application. In a recent work, authors have used MIMO radar with a 2D digital beamforming for simultaneous multi-target vital sign monitoring (Feng et al, 2021 ). The 2D digital beamforming helps finding the subject's chest location and forming individual narrow beams to measure respiration and heart rate accurately.…”

Section: Discussionmentioning

confidence: 99%

A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars

Han

Iglesias

et al. 2022

Front. Physiol.

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Respiration rate (RR) and respiration patterns (RP) are considered early indicators of physiological conditions and cardiorespiratory diseases. In this study, we addressed the problem of contactless estimation of RR and classification of RP of one person or two persons in a confined space under realistic conditions. We used three impulse radio ultrawideband (IR-UWB) radars and a 3D depth camera (Kinect) to avoid any blind spot in the room and to ensure that at least one of the radars covers the monitored subjects. This article proposes a subject localization and radar selection algorithm using a Kinect camera to allow the measurement of the respiration of multiple people placed at random locations. Several different experiments were conducted to verify the algorithms proposed in this work. The mean absolute error (MAE) between the estimated RR and reference RR of one-subject and two-subjects RR estimation are 0.61±0.53 breaths/min and 0.68±0.24 breaths/min, respectively. A respiratory pattern classification algorithm combining feature-based random forest classifier and pattern discrimination algorithm was developed to classify different respiration patterns including eupnea, Cheyne-Stokes respiration, Kussmaul respiration and apnea. The overall classification accuracy of 90% was achieved on a test dataset. Finally, a real-time system showing RR and RP classification on a graphical user interface (GUI) was implemented for monitoring two subjects.

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Section: Discussionmentioning

confidence: 99%

A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars

Han

Iglesias

et al. 2022

Front. Physiol.

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“…The information known a priori was the number of targets and their absolute distances (ranges) from the radar. Feng et al presented a MIMO CW radar for 2-D chest motion imaging from which the angular information of each subject can be determined 35 . The system can estimate the azimuth and elevation angles where the subjects’ chests are situated but it requires the information on the number of targets and their ranges.…”

Section: Introductionmentioning

confidence: 99%

Automatic radar-based 2-D localization exploiting vital signs signatures

Mercuri

Russo

Glassée

et al. 2022

Sci Rep

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In light of the continuously and rapidly growing senior and geriatric population, the research of new technologies enabling long-term remote patient monitoring plays an important role. For this purpose, we propose a single-input-multiple-output (SIMO) frequency-modulated continuous wave (FMCW) radar system and a signal processing technique to automatically detect the number and the 2-D position (azimuth and range information) of stationary people (seated/lying down). This is achieved by extracting the vital signs signatures of each single individual, separating the Doppler shifts caused by the cardiopulmonary activities from the unwanted reflected signals from static reflectors and multipaths. We then determine the number of human subjects present in the monitored environment by counting the number of extracted vital signs signatures while the 2-D localization is performed by measuring the distance from the radar where the vital signs information is sensed (i.e., locating the thoracic region). We reported maximum mean absolute errors (MAEs) of 0.1 m and 2.29$$^{\circ }$$ ∘ and maximum root-mean-square errors (RMSEs) of 0.12 m and 3.04$$^{\circ }$$ ∘ in measuring respectively the ranges and azimuth angles. The experimental validation demonstrated the ability of the proposed approach in monitoring paired human subjects in a typical office environment.

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“…Non-contact vital sign (VS) detection, such as respiration rate, built upon a radio frequency (RF)-based system has attracted a great deal of interest in recent years. It is used in a growing field of applications, such as healthcare and clinical assistance, sleep and baby monitoring, survivor rescue and research operations, through the wall human detection, and human activity classification [1][2][3][4][5][6][7][8][9]. Recently, the impulse response ultra wide-band (IR-UWB) radar has gained more attention thanks to its high-range resolution, good penetration capability, and low power consumption [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…The second one separates the VS signals based on the incident angle information. To this end, spatially distributed antennas have been exploited in designing the radar system, such as single-input multiple-output (SIMO) [8] and multiple-input multiple-output (MIMO) radars [6]. In particular, digital beamforming (DBF) plays an important role in beam scanning and beam focusing tasks thanks to its low-complexity structure, good control flexibility, and high accuracy [8].…”

Section: Introductionmentioning

confidence: 99%

Joint Vital Signs and Position Estimation of Multiple Persons Using SIMO Radar

et al. 2021

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Respiration rate monitoring using ultra-wideband (UWB) radar is preferred because it provides contactless measurement without restricting the person’s privacy. This study considers a novel non-contact-based solution using a single-input multiple-output (SIMO) UWB impulse radar. In the proposed system, the collected radar data are converted to several narrow-band signals using the generalized Goertzel algorithm (GGA), which are used as the input of the designed phased arrays for position estimation. In this context, we introduce the incoherent signal subspace methods (ISSM) for the direction of arrivals (DOAs) and distance evaluation. Meanwhile, a beam focusing approach is used to determine each individual and estimate their breathing rate automatically based on a linearly constrained minimum variance (LCMV) beamformer. The experimental results prove that the proposed algorithm can achieve high estimation accuracy in a variety of test environments, with an error of 2%, 5%, and 2% for DOA, distance, and respiration rate, respectively.

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Multitarget Vital Signs Measurement With Chest Motion Imaging Based on MIMO Radar

Cited by 37 publications

References 41 publications

A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars

A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars

Automatic radar-based 2-D localization exploiting vital signs signatures

Joint Vital Signs and Position Estimation of Multiple Persons Using SIMO Radar

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