Indoor Activity and Vital Sign Monitoring for Moving People with Multiple Radar Data Fusion

The monitoring of human activity and vital signs plays a significant role in remote health-care. Radar provides a non-contact monitoring approach without privacy and illumination concerns. However, multiple people in a narrow indoor environment bring dense multipaths for activity monitoring, and the received vital sign signals are heavily distorted with body movements. This paper proposes a framework based on Frequency Modulated Continuous Wave (FMCW) and Impulse Radio Ultra-Wideband (IR-UWB) radars to address these challenges, designing intelligent spatial-temporal information fusion for activity and vital sign monitoring. First, a local binary pattern (LBP) and energy features are extracted from FMCW radar, combined with the wavelet packet transform (WPT) features on IR-UWB radar for activity monitoring. Then the additional information guided fusing network (A-FuseNet) is proposed with a modified generative and adversarial structure for vital sign monitoring. A Cascaded Convolutional Neural Network (CCNN) module and a Long Short Term Memory (LSTM) module are designed as the fusion sub-network for vital sign information extraction and multisensory data fusion, while a discrimination sub-network is constructed to optimize the fused heartbeat signal. In addition, the activity and movement characteristics are introduced as additional information to guide the fusion and optimization. A multi-radar dataset with an FMCW and two IR-UWB radars in a cotton tent, a small room and a wide lobby is constructed, and the accuracies of activity and vital sign monitoring achieve 99.9% and 92.3% respectively. Experimental results demonstrate the superiority and robustness of the proposed framework.

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Matrix Pencil Method for Vital Sign Detection from Signals Acquired by Microwave Sensors

Sensors ◽

10.3390/s21175735 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5735

Author(s):

Somayyeh Chamaani ◽

Alireza Akbarpour ◽

Marko Helbig ◽

Jürgen Sachs

Keyword(s):

Vital Sign ◽

Continuous Wave ◽

Vital Signs ◽

Matrix Pencil ◽

Ultra Wideband ◽

Frequency Resolution ◽

High Temporal Resolution ◽

Matrix Pencil Method ◽

Mode Decomposition ◽

Microwave Sensors

Microwave sensors have recently been introduced as high-temporal resolution sensors, which could be used in the contactless monitoring of artery pulsation and breathing. However, accurate and efficient signal processing methods are still required. In this paper, the matrix pencil method (MPM), as an efficient method with good frequency resolution, is applied to back-reflected microwave signals to extract vital signs. It is shown that decomposing of the signal to its damping exponentials fulfilled by MPM gives the opportunity to separate signals, e.g., breathing and heartbeat, with high precision. A publicly online dataset (GUARDIAN), obtained by a continuous wave microwave sensor, is applied to evaluate the performance of MPM. Two methods of bandpass filtering (BPF) and variational mode decomposition (VMD) are also implemented. In addition to the GUARDIAN dataset, these methods are also applied to signals acquired by an ultra-wideband (UWB) sensor. It is concluded that when the vital sign is sufficiently strong and pure, all methods, e.g., MPM, VMD, and BPF, are appropriate for vital sign monitoring. However, in noisy cases, MPM has better performance. Therefore, for non-contact microwave vital sign monitoring, which is usually subject to noisy situations, MPM is a powerful method.

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Optimal Central Frequency for Non-Contact Vital Sign Detection Using Monocycle UWB Radar

Sensors ◽

10.3390/s20102916 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2916 ◽

Cited By ~ 2

Author(s):

Artit Rittiplang ◽

Pattarapong Phasukkit ◽

Teerapong Orankitanun

Keyword(s):

Vital Sign ◽

Vital Signs ◽

Ultra Wideband ◽

Central Frequency ◽

Additional Information ◽

Advantages And Disadvantages ◽

Uwb Radar ◽

Sign Detection ◽

Life Detection ◽

New Formula

Ultra-wideband (UWB) radar has become a critical remote-sensing tool for non-contact vital sign detection such as emergency rescues, securities, and biomedicines. Theoretically, the magnitude of the received reflected signal is dependent on the central frequency of mono-pulse waveform used as the transmitted signal. The research is based on the hypothesis that the stronger the received reflected signals, the greater the detectability of life signals. In this paper, we derive a new formula to compute the optimal central frequency to obtain as maximum received reflect signal as possible over the frequency up to the lower range of Ka-band. The proposed formula can be applicable in the optimization of hardware for UWB life detection and non-contact monitoring of vital signs. Furthermore, the vital sign detection results obtained by the UWB radar over a range of central frequency have been compared to those of the former continuous (CW) radar to provide additional information regarding the advantages and disadvantages of each radar.

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A Novel Vital-Sign Sensing Algorithm for Multiple Subjects Based on 24-GHz FMCW Doppler Radar

Remote Sensing ◽

10.3390/rs11101237 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1237 ◽

Cited By ~ 15

Author(s):

Hyunjae Lee ◽

Byung-Hyun Kim ◽

Jin-Kwan Park ◽

Jong-Gwan Yook

Keyword(s):

Vital Sign ◽

Continuous Wave ◽

Doppler Radar ◽

Spectral Estimation ◽

Estimation Method ◽

Vital Signs ◽

Phase Information ◽

Multiple Targets ◽

Wave Radar ◽

24 Ghz

A novel non-contact vital-sign sensing algorithm for use in cases of multiple subjects is proposed. The approach uses a 24 GHz frequency-modulated continuous-wave Doppler radar with the parametric spectral estimation method. Doppler processing and spectral estimation are concurrently implemented to detect vital signs from more than one subject, revealing excellent results. The parametric spectral estimation method is utilized to clearly identify multiple targets, making it possible to distinguish multiple targets located less than 40 cm apart, which is beyond the limit of the theoretical range resolution. Fourier transformation is used to extract phase information, and the result is combined with the spectral estimation result. To eliminate mutual interference, the range integration is performed when combining the range and phase information. By considering breathing and heartbeat periodicity, the proposed algorithm can accurately extract vital signs in real time by applying an auto-regressive algorithm. The capability of a contactless and unobtrusive vital sign measurement with a millimeter wave radar system has innumerable applications, such as remote patient monitoring, emergency surveillance, and personal health care.

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Vital Sign Monitoring Using FMCW Radar in Various Sleeping Scenarios

Sensors ◽

10.3390/s20226505 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6505

Author(s):

Emmi Turppa ◽

Juha M. Kortelainen ◽

Oleg Antropov ◽

Tero Kiuru

Keyword(s):

Heart Rate ◽

Remote Monitoring ◽

Vital Sign ◽

Continuous Wave ◽

Real Life ◽

Vital Signs ◽

Absolute Error ◽

Physical Contact ◽

Radar Signal ◽

Fmcw Radar

Remote monitoring of vital signs for studying sleep is a user-friendly alternative to monitoring with sensors attached to the skin. For instance, remote monitoring can allow unconstrained movement during sleep, whereas detectors requiring a physical contact may detach and interrupt the measurement and affect sleep itself. This study evaluates the performance of a cost-effective frequency modulated continuous wave (FMCW) radar in remote monitoring of heart rate and respiration in scenarios resembling a set of normal and abnormal physiological conditions during sleep. We evaluate the vital signs of ten subjects in different lying positions during various tasks. Specifically, we aim for a broad range of both heart and respiration rates to replicate various real-life scenarios and to test the robustness of the selected vital sign extraction methods consisting of fast Fourier transform based cepstral and autocorrelation analyses. As compared to the reference signals obtained using Embla titanium, a certified medical device, we achieved an overall relative mean absolute error of 3.6% (86% correlation) and 9.1% (91% correlation) for the heart rate and respiration rate, respectively. Our results promote radar-based clinical monitoring by showing that the proposed radar technology and signal processing methods accurately capture even such alarming vital signs as minimal respiration. Furthermore, we show that common parameters for heart rate variability can also be accurately extracted from the radar signal, enabling further sleep analyses.

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Adaptive Separation of Respiratory and Heartbeat Signals among Multiple People Based on Empirical Wavelet Transform Using UWB Radar

Sensors ◽

10.3390/s20174913 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4913 ◽

Cited By ~ 1

Author(s):

Mi He ◽

Yongjian Nian ◽

Luping Xu ◽

Lihong Qiao ◽

Wenwu Wang

Keyword(s):

Wavelet Transform ◽

Continuous Wave ◽

Body Movement ◽

Vital Signs ◽

Initial Boundary ◽

Tight Frame ◽

Ultra Wideband ◽

Clinical Monitoring ◽

Uwb Radar ◽

Empirical Wavelet Transform

The non-contact monitoring of vital signs by radar has great prospects in clinical monitoring. However, the accuracy of separated respiratory and heartbeat signals has not satisfied the clinical limits of agreement. This paper presents a study for automated separation of respiratory and heartbeat signals based on empirical wavelet transform (EWT) for multiple people. The initial boundary of the EWT was set according to the limited prior information of vital signs. Using the initial boundary, empirical wavelets with a tight frame were constructed to adaptively separate the respiratory signal, the heartbeat signal and interference due to unconscious body movement. To verify the validity of the proposed method, the vital signs of three volunteers were simultaneously measured by a stepped-frequency continuous wave ultra-wideband (UWB) radar and contact physiological sensors. Compared with the vital signs from contact sensors, the proposed method can separate the respiratory and heartbeat signals among multiple people and obtain the precise rate that satisfies clinical monitoring requirements using a UWB radar. The detection errors of respiratory and heartbeat rates by the proposed method were within ±0.3 bpm and ±2 bpm, respectively, which are much smaller than those obtained by the bandpass filtering, empirical mode decomposition (EMD) and wavelet transform (WT) methods. The proposed method is unsupervised and does not require reference signals. Moreover, the proposed method can obtain accurate respiratory and heartbeat signal rates even when the persons unconsciously move their bodies.

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Developing of Algorithms Monitoring Heartbeat and Respiration Rate of a Seated Person with an FMCW Radar

Advances in Radio Science ◽

10.5194/ars-19-195-2021 ◽

2021 ◽

Vol 19 ◽

pp. 195-206

Author(s):

Lorenz J. Dirksmeyer ◽

Aly Marnach ◽

Daniel Schmiech ◽

Andreas R. Diewald

Keyword(s):

Respiration Rate ◽

Continuous Wave ◽

Frequency Estimation ◽

Wavelet Packet ◽

Vital Signs ◽

Wave Mode ◽

Fine Tuning ◽

Fmcw Radar ◽

Real Time Visualization ◽

Spectral Frequency

Abstract. With a radar working in the 24 GHz ISM-band in a frequency modulated continuous wave mode the major vital signs heartbeat and respiration rate are monitored. The observation is hereby contactless with the patient sitting straight up in a distance of 1–2 m to the radar. Radar and sampling platform are components developed internally in the university institution. The communication with the radar is handled with MATLAB via TCP/IP. The signal processing and real-time visualization is developed in MATLAB, too. Cornerstone of this publication are the wavelet packet transformation and a spectral frequency estimation for vital sign calculation. The wavelet transformation allows a fine tuning of frequency subspaces, separating the heartbeat signal from the respiration and more important from noise and other movement. Heartbeat and respiration are monitored independently and compared to parallel recorded ECG-data.

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Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application

Sensors ◽

10.3390/s21072412 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2412

Author(s):

Sungwon Yoo ◽

Shahzad Ahmed ◽

Sun Kang ◽

Duhyun Hwang ◽

Jungjun Lee ◽

...

Keyword(s):

Vital Sign ◽

Continuous Wave ◽

Age Groups ◽

Vital Signs ◽

Group Classification ◽

Age Group ◽

Smart Vehicles ◽

Fmcw Radar ◽

Wide Range ◽

Age Group Classification

The ongoing intense development of short-range radar systems and their improved capability of measuring small movements make these systems reliable solutions for the extraction of human vital signs in a contactless fashion. The continuous contactless monitoring of vital signs can be considered in a wide range of applications, such as remote healthcare solutions and context-aware smart sensor development. Currently, the provision of radar-recorded datasets of human vital signs is still an open issue. In this paper, we present a new frequency-modulated continuous wave (FMCW) radar-recorded vital sign dataset for 50 children aged less than 13 years. A clinically approved vital sign monitoring sensor was also deployed as a reference, and data from both sensors were time-synchronized. With the presented dataset, a new child age-group classification system based on GoogLeNet is proposed to develop a child safety sensor for smart vehicles. The radar-recorded vital signs of children are divided into several age groups, and the GoogLeNet framework is trained to predict the age of unknown human test subjects.

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Through-Wall Multi-Subject Localization and Vital Signs Monitoring Using UWB MIMO Imaging Radar

Remote Sensing ◽

10.3390/rs13152905 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2905

Author(s):

Zhi Li ◽

Tian Jin ◽

Yongpeng Dai ◽

Yongkun Song

Keyword(s):

Continuous Wave ◽

Multiple Input Multiple Output ◽

Three Dimensional ◽

Vital Signs ◽

Low Frequency ◽

Mimo Radar ◽

Ultra Wideband ◽

Radar Images ◽

Imaging Radar ◽

Vital Signs Monitoring

Radar-based non-contact vital signs monitoring has great value in through-wall detection applications. This paper presents the theoretical and experimental study of through-wall respiration and heartbeat pattern extraction from multiple subjects. To detect the vital signs of multiple subjects, we employ a low-frequency ultra-wideband (UWB) multiple-input multiple-output (MIMO) imaging radar and derive the relationship between radar images and vibrations caused by human cardiopulmonary movements. The derivation indicates that MIMO radar imaging with the stepped-frequency continuous-wave (SFCW) improves the signal-to-noise ratio (SNR) critically by the factor of radar channel number times frequency number compared with continuous-wave (CW) Doppler radars. We also apply the three-dimensional (3-D) higher-order cumulant (HOC) to locate multiple subjects and extract the phase sequence of the radar images as the vital signs signal. To monitor the cardiopulmonary activities, we further exploit the VMD algorithm with a proposed grouping criterion to adaptively separate the respiration and heartbeat patterns. A series of experiments have validated the localization and detection of multiple subjects behind a wall. The VMD algorithm is suitable for separating the weaker heartbeat pattern from the stronger respiration pattern by the grouping criterion. Moreover, the continuous monitoring of heart rate (HR) by the MIMO radar in real scenarios shows a strong consistency with the reference electrocardiogram (ECG).

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

Electronics ◽

10.3390/electronics10222805 ◽

2021 ◽

Vol 10 (22) ◽

pp. 2805

Author(s):

Ibrahim Kakouche ◽

Hamza Abadlia ◽

Mohammed Nabil El Korso ◽

Ammar Mesloub ◽

Abdelmadjid Maali ◽

...

Keyword(s):

Respiration Rate ◽

Vital Signs ◽

Radar Data ◽

Minimum Variance ◽

Position Estimation ◽

Ultra Wideband ◽

Estimation Accuracy ◽

Contactless Measurement ◽

Beam Focusing ◽

Input Multiple Output

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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Random-Noise Denoising and Clutter Elimination of Human Respiration Movements Based on an Improved Time Window Selection Algorithm Using Wavelet Transform

Sensors ◽

10.3390/s19010095 ◽

2018 ◽

Vol 19 (1) ◽

pp. 95 ◽

Cited By ~ 6

Author(s):

Farnaz Shikhsarmast ◽

Tingting Lyu ◽

Xiaolin Liang ◽

Hao Zhang ◽

Thomas Gulliver

Keyword(s):

Time Window ◽

Human Subjects ◽

Wavelet Packet ◽

Random Noise ◽

Vital Signs ◽

Ultra Wideband ◽

Multiple Time ◽

Selection Algorithm ◽

Human Respiration ◽

Window Selection

This paper considers vital signs (VS) such as respiration movement detection of human subjects using an impulse ultra-wideband (UWB) through-wall radar with an improved sensing algorithm for random-noise de-noising and clutter elimination. One filter is used to improve the signal-to-noise ratio (SNR) of these VS signals. Using the wavelet packet decomposition, the standard deviation based spectral kurtosis is employed to analyze the signal characteristics to provide the distance estimate between the radar and human subject. The data size is reduced based on a defined region of interest (ROI), and this improves the system efficiency. The respiration frequency is estimated using a multiple time window selection algorithm. Experimental results are presented which illustrate the efficacy and reliability of this method. The proposed method is shown to provide better VS estimation than existing techniques in the literature.

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