Non-contact Vital Sign Monitoring Research Articles

Mapping knowledge landscapes and emerging trends of non-contact vital signs monitoring: A bibliometric and visualization analysis from 2002 to 2023

This study makes a comparative analysis based on knowledge mapping of Chinese and English publications on non-contact vital signs monitoring and provides a reference for future research and development. Publications were retrieved from Web of Science (WoS) and PubMed, China National Knowledge Infrastructure (CNKI), and Wanfang databases from January 1, 2002 to October 31, 2023. Bibliometric analysis was performed using CiteSpace software. Information from 612 English and 543 Chinese original research articles was collected and analyzed. The maximum number of English appeared in 2022 and the maximum number of Chinese articles appeared in 2020, and the number of articles published afterward declined slightly. For the articles published in English, USA and China shared the highest number of publications. Chinese Academy of Sciences was the most published institution. Steffen Leonhardt was the highest-published author. Meanwhile, within the Chinese articles, the most productive province was Beijing. The most productive institution was the Air Force Medical University. The most productive author was Weixue Liu. The research institutions and authors of English and Chinese articles were decentralized. The hotspots of the English articles mainly were “heart rate” and “respiratory rate”, and the Chinese articles focused on “temperature” and “heart rate”. Concluding, the research on non-contact vital signs monitoring is in a booming phase of development at present. Non-contact heart rate monitoring is a popular research direction in English and Chinese research. In the future, research institutions and authors should strengthen their cooperation and communication to promote the development of this research area.

Analysis of Spectral Estimation Algorithms for Accurate Heart Rate and Respiration Rate Estimation Using an Ultra-Wideband Radar Sensor.

Non-contact vital sign monitoring has been an important research topic recently due to the ability to monitor patients for an extended period especially during sleep without requiring uncomfortable attachments. Radar is a popular sensor for vital sign monitoring research. Various algorithms have been proposed for estimating respiration rate and heart rate from the radar data. But many algorithms rely on Fast Fourier Transform (FFT) to convert time domain signal to the frequency domain and estimate vital signs, despite FFT having limitation of frequency resolution being inverse of the time interval of data sample. However, there are other spectral estimation algorithms, which have not been much researched into the suitability of vital sign estimation using radar signals. In this paper, we compared eight different types of spectral estimation algorithms, including FFT, for respiration rate and heart rate estimation of stationary subjects in a controlled environment. The evaluation is based on extensive data consisting of different stationary subject positions. Considering the results, the eligibility of algorithms other than FFT for respiration rate and heart rate estimation is demonstrated. Using this work, researchers can get an overview on which algorithm is suitable for their work without the need to review individual algorithms separately.

A Novel Frequency-Tracking Algorithm for Noncontact Vital Sign Monitoring

A Novel Frequency-Tracking Algorithm for Noncontact Vital Sign Monitoring

Remote photoplethysmography-based human vital sign prediction using cyclical algorithm.

This article aims to predict vital signs like heart rate (HR), respiration rate, and arterial oxygen saturation using ambient light video, eliminating chronic distortions through improved frame quality with BER estimation. The study employs the cascade residual CNN-FPNR technique for preprocessing and SNR enhancement using energy variance maximization. The image cascade network (ICNet) facilitates segmentation, achieving strong segmentation in low-light ambient videos. Remote photoplethysmography (iPPG) enables noncontact vital sign monitoring, predicting HR and respiratory rate (RR). An innovative noninvasive temperature and cyclical algorithm, incorporating principal component analysis and fast Fourier transform, evaluate patient HR and RR. To address challenges related to involuntary movements, a dynamic time-warping-based optimization method is used for precise region selection. The study introduces an intensity variance-based threshold analysis for arterial oxygen saturation level determination. Ultimately, the support vector machine (SVM) classification technique evaluates the ground truth, showcasing the system's promising potential for remote and accurate vital sign assessment.

A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring.

Noncontact vital sign monitoring based on radar has attracted great interest in many fields. Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. This paper proposes a new algorithm for HRV monitoring in which frequency-modulated continuous-wave (FMCW) radar is used to separate echo signals from different distances, and the beamforming technique is adopted to improve signal quality. After the phase reflecting the chest wall motion is demodulated, the acceleration is calculated to enhance the heartbeat and suppress the impact of respiration. The time interval of each heartbeat is estimated based on the smoothed acceleration waveform. Finally, a joint optimization algorithm was developed and is used to precisely segment the acceleration signal for analyzing HRV. Experimental results from 10 participants show the potential of the proposed algorithm for obtaining a noncontact HRV estimation with high accuracy. The proposed algorithm can measure the interbeat interval (IBI) with a root mean square error (RMSE) of 14.9 ms and accurately estimate HRV parameters with an RMSE of 3.24 ms for MEAN (the average value of the IBI), 4.91 ms for the standard deviation of normal to normal (SDNN), and 9.10 ms for the root mean square of successive differences (RMSSD). These results demonstrate the effectiveness and feasibility of the proposed method in emotion recognition, sleep monitoring, and heart disease diagnosis.

Sparsity-Based Multi-Person Non-Contact Vital Signs Monitoring via FMCW Radar.

Non-contact technology for monitoring the vital signs of multiple individuals, such as respiration and heartbeat, has been investigated in recent years due to the rising cardiopulmonary morbidity, the risk of disease transmission, and the heavy burden on medical staff. Frequency-modulated continuous wave (FMCW) radars have shown great promise in meeting these needs, even using a single-input-single-output (SISO) setup. However, contemporary techniques for non-contact vital signs monitoring (NCVSM) via SISO FMCW radar, are based on simplistic models and present difficulties in coping with noisy environments containing multiple objects. In this work, we first develop an extended model for multi-person NCVSM via SISO FMCW radar. Then, by utilizing the sparse nature of the modeled signals in conjunction with human-typical cardiopulmonary features, we present accurate localization and NCVSM of multiple individuals in a cluttered scenario, even with only a single channel. Specifically, we provide a joint-sparse recovery mechanism to localize people and develop a robust method for NCVSM called Vital Signs-based Dictionary Recovery (VSDR), which uses a dictionary-based approach to search for the rates of respiration and heartbeat over high-resolution grids corresponding to human cardiopulmonary activity. The advantages of our method are illustrated through examples that combine the proposed model with in-vivo data of 30 individuals. We demonstrate accurate human localization in a noisy scenario that includes both static and vibrating objects and show that our VSDR approach outperforms existing NCVSM techniques based on several statistical metrics. The findings support the widespread use of FMCW radars with the proposed algorithms in healthcare.

Robust Radar-Based Vital Sensing With Adaptive Sinc Filtering and Random Body Motion Rejections

In this paper, we propose a non-contact vital sign monitoring algorithm based on 60GHz Frequency Modulated Continuous Wave (FMCW) radar. At the heart of the algorithm is a bank of sinc filters covering the heart and respiratory frequencies of an healthy adult. The filter bank is adapted, in successive iterations, to the specific cardio-respiratory frequencies of the monitored subject. The adaptability of the algorithm ensures an accurate estimate of Heart Rate (HR) and Respiratory Rate(RR) for a subject sitting in front of the radar at a distance between 0.3m to 1.1m while being robust to minor body movements and harmonics. The efficiency of the solution is demonstrated on an experimental data set including recordings of 10 subjects, where we obtain an Root Mean Square Error (RMSE) of 6.16 bpm for HR and 2.09 rpm for RR.

A Feasibility Study of Remote Non-Contact Vital Signs (NCVS) Monitoring in a Clinic Using a Novel Sensor Realized by Software-Defined Radio (SDR)

The COVID-19 outbreak has caused panic around the world as it is highly infectious and has caused about 5 million deaths globally. A robust wireless non-contact vital signs (NCVS) sensor system that can continuously monitor the respiration rate (RR) and heart rate (HR) of patients clinically and remotely with high accuracy can be very attractive to healthcare workers (HCWs), as such a system can not only avoid HCWs' close contact with people with COVID-19 to reduce the infection rate, but also be used on patients quarantined at home for telemedicine and wireless acute-care. Therefore, we developed a custom Doppler-based NCVS radar sensor system operating at 2.4 GHz using a software-defined radio (SDR) technology, and the novel biosensor system has achieved impressive real-time RR/HR monitoring accuracies within approximately 0.5/3 breath/beat per minute (BPM) on student volunteers tested in our engineering labs. To further test the sensor system's feasibility for clinical use, we applied and obtained an Internal Review Board (IRB) approval from Texas Tech University Health Sciences Center (TTUHSC) and have used this NCVS monitoring system in a doctor's clinic at TTUHSC; following testing on 20 actual patients for a small-scale clinical trial, we have found that the system was still able to achieve good NCVS monitoring accuracies within ~0.5/10 BPM across 20 patients of various weight, height and age. These results suggest our custom-designed NCVS monitoring system may be feasible for future clinical use to help combatting COVID-19 and other infectious diseases.

Respiration Monitoring in High-Dynamic Environments via Combining Multiple WiFi Channels Based on Wire Direct Connection Between RX/TX

As one widely applied wireless technique, WiFi has the potential to execute noncontact monitoring of vital signs based on channel state information (CSI). However, due to the dynamic of the surrounding environment, the bandwidth of the WiFi channel is not enough to identify the respiration-induced path from other movement-induced paths and this seriously limits the accuracy of respiration rate detection. In this article, we propose ExRadio, a system that can monitor respiration in high-dynamic environments via combining multiple WiFi channels. Specifically, the receiver synchronously switches the channels with the transmitter and samples CSI at multiple channels, and then the CSI data are combined and regarded as CSI data of one extended-bandwidth channel. However, the hardware-related noises from multiple channels are also accumulated. Eliminating these noises causes too heavy computation overhead to be afforded by the embedded devices and affects the real-time performance of respiration monitoring. To address this problem, we propose an effective approach that employs the ratio of CSI readings from the wireless channel and wire direct connection channel to shorten the time of eliminating the hardware-related noise. We deploy the ExRadio in commercial off-the-shelf embedded devices and conduct a series of experiments. The experimental results demonstrate that reducing the execution time is beneficial to respiration rate detection under high-dynamic environments, and the overall detection error of ExRadio is less than 0.5 bpm even when multiple persons who are 1.5-m away from the monitored person are fast walking.

Non-contact Vital Signs Monitoring in Paediatric Anaesthesia - Current Challenges and Future Direction.

Non-contact vital sign monitoring is an area of increasing interest in the clinical scenario since it offers advantages over traditional monitoring using leads and wires. These advantages include reduction in transmission of infection and more freedom of movement. Yet there is a paucity of studies available in the clinical setting particularly in paediatric anaesthesia. This scoping review aims to investigate why contactless monitoring, specifically with red-green-blue cameras, is not implemented in mainstream practise. The challenges, drawbacks and limitations of non-contact vital sign monitoring, will be outlined, together with future direction on how it can potentially be implemented in the setting of paediatric anaesthesia, and in the critical care scenario.

HeRe: Heartbeat Signal Reconstruction for Low-Power Millimeter-Wave Radar Based on Deep Learning

The non-contact monitoring of vital signs without body-attached sensors is a promising home healthcare technique. Recent studies have shown that millimeter-wave (mmW) radar can be used to continuous daily monitoring of vital signs such as heart rate (HR) and respiratory rate (RR). To minimize disruption to the user’s daily life and accommodate for home environments, low-power radar or radar with antennas in package (AiP) is becoming increasingly popular. However, the lower transmitting power of these systems can lead to decreased Signal-to-Noise Ratio (SNR) of the radar signal, making established measurement methods ineffective. The vital signs, particularly HR, are no longer accurately measured due to the small amplitude associated with heartbeats. In this work, we present HeRe for low-power radar, a deep learning approach for heartbeat signal reconstruction. The proposed method utilizes a neural network to identify patterns in the signal, leading to a significant improvement in the SNR of the heartbeat signal. By leveraging historical data, the current HR estimation is further refined, yielding improved precision in the results. Experiments have demonstrated the efficacy of signal reconstruction for improving the heartbeat signal in both the time and frequency domains. A comparison of the proposed approach to related works on various HR distributions, sensing ranges, and subjects showed significantly higher accuracy. To further assess the robustness of the proposed approach, extended continuous monitoring was performed and 97.5% accuracy was achieved. This article highlights the advantages of deep learning techniques for non-contact, radar-based HR monitoring, leading to accurate HR monitoring with the lowest power consumer-grade millimeter wave radar currently available on the market.

Multitarget Respiration Monitoring Based on Cumulative Phase Gradient Approach

Simultaneous measurements of multi-target vital signs have been quite challenging for radar-based non-contact vital signs monitoring. This paper presents a novel cumulative phase gradient approach for accurate estimates of multi-target respirations without either range or beam discrimination in a single-input single-output (SISO) radar sensor. The signal model of multi-target respiration is first provided, and then a cumulative phase gradient approach is developed to accurately estimate multi-target respirations from the observed mixtures by exploiting the underlying periodicity of the respiration waveforms. The proposed method provides average estimated error rates around 3% in the experiment with closely located three human targets within range resolution, and demonstrates its significant superiorities over other methods.

IQ Signal Demodulation for Noncontact Vital Sign Monitoring Using a CW Doppler Radar: A Review

Using a Doppler radar for noncontact detection is a promising way to monitor vital signs of a subject more conveniently than other common contact medical devices, but has many challenges including null-point issue, dc offset, and nonlinear distortion. The wireless property of a radar system easily affects the Doppler signals, so it needs a demodulation method to obtain the vital sign information. Furthermore, detecting precise information for respiration rate (RR) and heart rate (HR) of the subject needs further signal processing to extract the target signals from the demodulated signals at various interference levels. Hence, this study reviews research on continuous-wave (CW) Doppler radar, signal demodulation and subsequent signal processing to address the problems for noncontact vital sign monitoring.

Non-Contact VITAL Signs Monitoring of a Patient Lying on Surgical Bed Using Beamforming FMCW Radar.

Respiration and heartrates are important information for surgery. When the vital signs of the patient lying prone are monitored using radar installed on the back of the surgical bed, the surgeon's movements reduce the accuracy of these monitored vital signs. This study proposes a method for enhancing the monitored vital sign accuracies of a patient lying on a surgical bed using a 60 GHz frequency modulated continuous wave (FMCW) radar system with beamforming. The vital sign accuracies were enhanced by applying a fast Fourier transform (FFT) for range and beamforming which suppress the noise generated at different ranges and angles from the patient's position. The experiment was performed for a patient lying on a surgical bed with or without surgeon. Comparing a continuous-wave (CW) Doppler radar, the FMCW radar with beamforming improved almost 22 dB of signal-to-interference and noise ratio (SINR) for vital signals. More than 90% accuracy of monitoring respiration and heartrates was achieved even though the surgeon was located next to the patient as an interferer. It was analyzed using a proposed vital signal model included in the radar IF equation.

Localized Surface Plasmons on Textiles for Non-Contact Vital Sign Sensing

Radio frequency technologies are capable of remotely sensing human vital signs for broad applications in healthcare. However, their use during daily life is hindered by challenges such as environmental interference, low sensitivity, and nonideal form factors. Here, we report a radio frequency sensor made from a conductive textile that provides sensitive noncontact monitoring of vital signs. The sensor (13 cm2 in size) comprises a radial pattern of grooves that support spoof localized surface plasmon (LSP) modes and a ground plane with a complementary window that controls the sensor’s interaction with the body. These spoof LSP modes provide a sharp resonant dip and a broadly tunable evanescent field that enables tracking of heartbeat and respiration via the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$|S_{11}|$ </tex-math></inline-formula> spectrum measured in a connector-free manner. We establish the optimal operating mode of the sensor and show that it provides a resonant frequency shift of 250 MHz for respiration and 100 MHz for heartbeat motions. Experiments with a healthy human subject show that the measured heart rate (HR) and respiration rate (RR) are in close agreement with gold standard sensors and are robust to daily activities performed while sitting.

Accurate contactless sleep apnea detection framework with signal processing and machine learning methods.

The detection of sleep apnea is critical for assessing sleep quality. It is also a proven biometric in diagnosing cardiovascular and other diseases. Recent studies have shown that radar-based non-contact vital sign monitoring system can effectively detect sleep apnea. However, the detection accuracy in the current study still needs to be improved. In this paper, we propose a sleep apnea detection framework based on FMCW radar. First, the radar system is employed to record the sleep data throughout the night with polysomnography (PSG) comparison. Then, in order to extract more accurate respiratory signal from the raw radar data, the signal processing methods are investigated to solve the observed discontinuity phenomenon. Finally, machine learning methods are adopted. The apneic and not-apneic events are classified accurately by selecting effective features of respiratory signal. As shown in the experimental results, the proposed system could achieve a good classification performance with an accuracy of 95.53%, a sensitivity of 72.60%, a specificity of 97.32%, a Kappa of 0.68, and an F-score of 0.84.

Datamining and Its Applications

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Survey, Analysis and Comparison of Radar Technologies for Embedded Vital Sign Monitoring.

Contactless vital sign monitoring systems are becoming increasing in demand for a wide range of biomedical applications. Millimetre-wave radars and embedded signal processing are the most promising technologies to enable non-contact vital signs monitoring. In this work, the challenging task of heart rate estimation from radar data has been addressed. Three different radar systems from Infineon, Texas Instruments and Acconeer, and four algorithms, FFT, Median-FFT, STFT and Median-STFT, have been analysed and compared against a reference sensor. Accuracy, as well as power figures, have been reported for all the radar systems. A dataset of 16 volunteers has been acquired, yielding a total of 400 minutes of radar-recorded vital sign data. The accuracy of the four investigated algorithms has been reported on average and per subject for every radar. The algorithm exploiting the Short Time Fourier Transform (STFT) is able to achieve an error as low as 0.02% on a single person and of 6.4% in heart rate estimation on average across the whole dataset.

Non-contact automatic vital signs monitoring of neonates in NICU using video camera imaging

ABSTRACT Non-contact vital signs monitoring is desirable in both clinical and non-clinical sectors. However, there are limited camera-based studies to monitor the vital signs of neonatal infants in a clinical setting. In this study, a digital camera was used to remotely monitor the heart rate (HR) and respiratory rate (RR) of seven infants at Flinders Medical Centre Neonatal Intensive Care Unit (NICU). The region-of-interest (ROI) was automatically selected based on a skin detection method using skin hue. The experimental results have been validated with the reference data obtained from an ECG monitor, and they showed a strong correlation using the Pearson correlation coefficient, (PCC) .9836 and .9426 for HR and RR, respectively, and a lower error rate with RMSE 2.66 beats/min and 2.93 breaths/min between measured data and reference data. A Bland Altman analysis of the data also presented a close correlation between measured data and reference data for both HR and RR with a mean bias .69 beats/min and .28 breaths/min, and the lower and upper limit of agreement −4.4 to +5.8 beats/min and −5.5 to +6.1 breaths/min, respectively. Therefore, video camera imaging may replace conventional monitoring in NICU and has potential applications in other contexts such as home health monitoring.

Movement Compensated Driver’s Respiratory Rate Extraction

In non-contact vital sign monitoring using radar, radar signal distorted by the surrounding unspecified factors is unsuitable for monitoring vital signs. In order to monitor vital signs accurately, it is essential to compensate for distortion of radar signals caused by surrounding environmental factors. In this paper, we propose a driver vital signal compensation method in driving situations, including the driver’s movements using a frequency-modulated continuous-wave (FMCW) radar. Driver’s movement is quantified from the radar signal and used to set a distortion signal compensation index to compensate for the signal distortion induced in the driving situation that the driver’s movement occurs. The experimental results show that the respiration rate estimated from the radar signal compensated through the proposed method is similar to the actual respiration rate than from the signal before calibration. These results confirm the possibility of using the proposed method in a non-statistic situation and effectiveness in estimating respiration rate reflecting human movement in monitoring vital signs using FMCW radar.