Non-contact Vital Sign Research Articles

Enhancing Contactless Respiratory Rate Measurement Accuracy: Integration of 24GHz FMCW Radar and XGBoost Machine Learning

Advancements in non-contact vital sign monitoring are crucial for enhancing patient measurements' accuracy and overall patient experiences. This research explores the integration of 24GHz Frequency-Modulated Continuous-Wave (FMCW) radar with the XGBoost machine learning algorithm to improve the detection of respiratory rate (RR). This innovative approach offers a promising alternative to traditional contact-based methods. The study utilizes FMCW radar to detect respiratory motion, while signal patterns are analyzed using XGBoost to ensure accuracy across various healthcare environments. The method involves collecting signals, pre-processing to remove noise and irrelevant data, and extracting features to be analyzed by the XGBoost algorithm. The collected dataset, which includes controlled and randomized respiratory rates from a diverse subject pool, establishes a solid basis for the algorithm's training and validation, ensuring extensive adaptability and precision. Empirical results show that XGBoost surpasses other machine learning models' accuracy and reliability. Importantly, this method significantly reduces error margins compared to established benchmarks, leading to substantial improvements in RR measurement. The implications of this study are wide-ranging, indicating that such a system could significantly enhance patient care standards by providing continuous, accurate, and non-intrusive monitoring, especially in settings where traditional methods are impractical or uncomfortable. Future research should aim to refine the system's real-world applicability, assess long-term reliability, and optimize the technology for integration into existing healthcare frameworks, thereby further transforming the landscape of patient monitoring technologies.

Emotion Recognition in a Closed-Cabin Environment: An Exploratory Study Using Millimeter-Wave Radar and Respiration Signals

In the field of psychology and cognition within closed cabins, noncontact vital sign detection holds significant potential as it can enhance the user’s experience by utilizing objective measurements to assess emotions, making the process more sustainable and easier to deploy. To evaluate the capability of noncontact methods for emotion recognition in closed spaces, such as submarines, this study proposes an emotion recognition method that employs a millimeter-wave radar to capture respiration signals and uses a machine-learning framework for emotion classification. Respiration signals were collected while the participants watched videos designed to elicit different emotions. An automatic sparse encoder was used to extract features from respiration signals, and two support vector machines were employed for emotion classification. The proposed method was experimentally validated using the FaceReader software, which is based on audiovisual signals, and achieved an emotion classification accuracy of 68.21%, indicating the feasibility and effectiveness of using respiration signals to recognize and assess the emotional states of individuals in closed cabins.

Vital signs detection of moving targets using FMCW radar

Abstract Respiratory and heartbeat rates are crucial indicators for human health assessment. Compared to contact-based measurements, millimeter-wave radar detection of these vital signs avoids the discomfort caused by physical contact and better protects personal privacy, making it highly valuable for home health monitoring. The use of millimeter-wave radar for vital sign detection of the human body is mostly focused on targets in a stationary state at present. However, the human body may sway or even move during actual detection. This article proposes a non-contact vital sign detection method for moving targets. Compared with methods for detecting vital signs of stationary targets, detecting vital signs of moving targets requires determining the range bin where the targets are located continuously and extracting target phase information. The noise components such as movement and sway contained in the phase signal need to be removed. In this paper, moving target indication is used to remove static components, an adaptive range bin selection method is proposed to determine the range bin where the targets are located, and range bin selection fluctuation is smoothed using a moving average filter. The wavelet transform is used to decompose the phase signal, remove swaying noise based on autocorrelation function, and reconstruct the life signal for different scale factors. A bandpass filter is used to separate the respiratory and heartbeat signals, and a notch filter is designed to suppress respiratory harmonic signals. The experimental results show that the proposed method can separate vital signs signals from the phase signals of moving targets, achieving detection of respiration and heartbeat rate. The average accuracy of respiration and heartbeat rate detection is 94.7% and 95.5%, respectively.

Cutting-Edge Microwave Sensors for Vital Signs Detection and Precise Human Lung Water Level Measurement

In this article, a comprehensive review is presented of recent technological advancements utilizing electromagnetic sensors in the microwave range for detecting human vital signs and lung water levels. With the main objective of improving detection accuracy and system robustness, numerous advancements in front-end architecture, detection techniques, and system-level integration have been reported. The benefits of non-contact vital sign detection have garnered significant interest across a range of applications, including healthcare monitoring and search and rescue operations. Moreover, some integrated circuits and portable systems have lately been shown off. A comparative examination of various system architectures, baseband signal processing methods, system-level integration strategies, and possible applications are included in this article. Going forward, researchers will continue to focus on integrating radar chips to achieve compact form factors and employ advanced signal processing methods to further enhance detection accuracy.

Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System.

An increasing number of studies on non-contact vital sign detection using radar are now beginning to turn to data-driven neural network approaches rather than traditional signal-processing methods. However, there are few radar datasets available for deep learning due to the difficulty of acquiring and labeling the data, which require specialized equipment and physician collaboration. This paper presents a new model of heartbeat-induced chest wall motion (CWM) with the goal of generating a large amount of simulation data to support deep learning methods. An in-depth analysis of published CWM data collected by the VICON Infrared (IR) motion capture system and continuous wave (CW) radar system during respiratory hold was used to summarize the motion characteristics of each stage within a cardiac cycle. In combination with the physiological properties of the heartbeat, appropriate mathematical functions were selected to describe these movement properties. The model produced simulation data that closely matched the measured data as evaluated by dynamic time warping (DTW) and the root-mean-squared error (RMSE). By adjusting the model parameters, the heartbeat signals of different individuals were simulated. This will accelerate the application of data-driven deep learning methods in radar-based non-contact vital sign detection research and further advance the field.

Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning.

The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value.

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.

Robust Method for Non-Contact Vital Sign Measurement in Videos Acquired in Real-World Light Settings From Skin Less Affected by Blood Perfusion

Robust Method for Non-Contact Vital Sign Measurement in Videos Acquired in Real-World Light Settings From Skin Less Affected by Blood Perfusion

Non-Contact Vital Sign Detection With High Noise and Clutter Immunity Based on Coherent Low-IF CW Radar

Non-Contact Vital Sign Detection With High Noise and Clutter Immunity Based on Coherent Low-IF CW Radar

Prospective clinical validation of a noncontact vital signs measurement smartphone application in emergency department

Abstract Background Remote photoplethysmography (rPPG) is a noncontact optical measurement technique that can be used to detect blood volume changes in the microvascular bed of tissue, using a RGB camera. We developed a smartphone application that leverages rPPG in order to monitor the heart rate and respiratory rate of patients using a 30s video selfie. Due to the widespread availability of cameras in smartphones, tablets and laptops, such an application allows to automate noncontact physiological monitoring and recording, improving the quality and accessibility of this essential clinical information. Purpose This study aimed to evaluate the accuracy of such remote measurements compared with regular contact based measurement methods (pulse oximeter) in real-life clinical settings. Methods We recruited 443 patients who consulted the emergency department in 4 French hospitals. After obtaining written informed consent, the patients filled in a questionnaire collecting demographic information including age, gender and the Fitzpatrick skin phototype. Each patient was asked to sit in front of the tablet, with the reference pulse oximeter installed on their finger. All measurements were 30s long. When starting the measurement, the medical staff started to count the patient's respiratory cycles. After 15s, the first COBOX result was available, and then updated every second. At the end of the 30s, the medical staff reported the number of respiratory cycles which were doubled to obtain a respiratory rate in cycles per minute (cpm). Results The mean age was 44 ± 18 years old, 45% was male. A total of 6311 pairs of values of heart rate taken by COBOX (HRCOBOX) and by the reference pulse oximeter (HRreference) and 453 pairs of values of respiratory rate taken by COBOX (RRCOBOX) and by the reference manual count (RRreference) were collected. The proportion of heart rates > 100bpm (beats per minute) was 4.4% and of respiratory rates > 20cpm was 22.9%. The number of insufficient quality measurements was 838 for heart rate (13%). The medical staff could not count reliably the respiratory rate in 20 sessions (4.4%). 30 sessions (6.9%) had a measurement of insufficient quality. For the heart rate, the Pearson correlation between COBOX and the reference was 97.2%. The root mean square error (RMSE) was 3.1bpm. For the respiratory rate, the Pearson correlation between COBOX and the reference was 78.3%. The RMSE was 2.9cpm. Conclusion Our work aimed to evaluate the accuracy of such remote measurements compared with existing contact point measurement methods in real-life clinical settings. Our results described a very good accuracy and correlation between the rPPG system and the gold standard, thus paving the way for more precise and more accessible remote patient monitoring. Such a technology has the potential to be transformative for telemedicine, especially in medical deserts where the access to both physicians and medical devices is lacking.Heart rate resultsRespiratory rate results

Accurate multi-target vital signs detection method for FMCW radar

The frequency modulated continuous wave (FMCW) radar has gained increasing attention in the field of noncontact vital signs detection. However, the detection of the target chest’s range bin is easily influenced by various clutters and interferences, posing numerous challenges to vital signs detection, particularly in scenarios involving multiple targets This paper proposes an anti-clutter and interference method that combinates moving target indication (MTI), constant false alarm rate (CFAR) detection, K-means clustering, and local search with moving average filtering to accurately detect multi-target vital signs. Moreover, it employs the extended differentiate and cross multiply algorithm for phase demodulation to overcome phase discontinuity. The proposed method is evaluated using a 77 GHz FMCW radar in an office environment. Experimental results demonstrate that the proposed method can effectively detect multiple targets amidst various clutters and small random body movement, yielding mean accuracies of 94.2 % and 95.1 % for breathing rate and heartbeat rate, respectively.

A Novel Frequency-Tracking Algorithm for Noncontact Vital Sign Monitoring

A Novel Frequency-Tracking Algorithm for Noncontact Vital Sign Monitoring

A new approach to contactless probing of vital signs

Vital sign detection is used across ubiquitous scenarios in medical and health settings and contact and wearable sensors have been widely deployed. Contactless detection can be achieved using camera imaging, but it is susceptible to ambient light conditions along with privacy concerns. This brief report appraises a photonic radar for non-contact vital sign detection. The high resolution of the radar system enables accurate respiratory detection from breathing simulators and a cane toad as a human proxy. This could cater for contactless and high-resolution vital sign detection to meet the increasing demands of future medical and healthcare applications.

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.

Assessments of perioperative respiratory pattern with non-contact vital sign monitor in children undergoing minor surgery: a prospective observational study.

Nurses routinely assess respiration of hospitalized children; however, respiratory rate measurements are technically difficult due to rapid and small chest wall movements. The aim of this study is to reveal the respiratory status of small children undergoing minor surgery with load cells placed under the bed legs, and to test the hypothesis that respiratory rate (primary variable) is slower immediately after arrival to the ward and recovers in 2h. Continuous recordings of the load cell signals were performed and stable respiratory waves within the 10 discriminative perioperative timepoints were used for respiratory rate measurements. Apnea frequencies were calculated at pre and postoperative nights and 2h immediately after returning to the ward after surgery. Continuous recordings of the load cell signals were successfully performed in 18 children (13 to 119months). Respiratory waves were appraisable for more than 70% of nighttime period and 40% of immediate postoperative period. There were no statistically significant differences of respiratory rate in any timepoint comparisons (p = 0.448), thereby not supporting the study hypothesis. Respiratory rates changed more than 5 breaths per minute postoperatively in 5 out of 18 children (28%) while doses of fentanyl alone did not explain the changes. Apnea frequencies significantly decreased 2h immediately after returning to the ward and during the operative night compared to the preoperative night. Respiratory signal extracted from load cell sensors under the bed legs successfully revealed various postoperative respiratory pattern change in small children undergoing minor surgery. UMIN (University Hospital Information Network) Clinical Registry: UMIN000045579 ( https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000052039 ).

Contactless methods to acquire heart and respiratory signals—A review

<p align="justify">The vital sign is the most important parameter for the internal health status of any subject in time. Every person is witnessed of COVID-19 global pandemic viruses. The world population has faced this problem globally. Collecting the infected person’s sample data in a contact-based approach may lead to the spreading of the disease. On the other hand, if we use a non-contact-based approach for the collection, it is somehow far better and breaks the chain of virus spreading. This radar-based technique is preferred in non-contact vital sign detection so that any person gets to their health status prior and according to that doctor can diagnose the proper treatment. The radar-based signal is targeted to the subject’s chest. Due to the chest wall displacement main vital sign parameters of the heart and respiration of the individual’s health are being captured. These captured signals are called vital signs, with this it is very helpful that the pre-diagnosis and treatment can be recommended by doctors or health service providers. Some patients due to their movement may be older or children for a long-time use skin irritation or allergy type of problems may face. On the other hand, some patients may be COVID-19 infected disease and burn patients. Hence, it is not possible to connect as both cases are unexpected for the required purpose. For constant and continuous measurement, existing contact-based methods are not fruitful hence non-contact-based approach is adopted. Non-contact-based vital sign detection is preferably due to several problems occurring. This paper presents a state-of-the-art review of recent monitoring methods and techniques for health monitoring in medical fields of operations. These methods and techniques are used as a tool to acquire, visualize and analyze the sampled data collected in any environment either indoor or outdoor.</p>

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.

Photonic radar for contactless vital sign detection

Vital sign detection is used across ubiquitous scenarios in medical and health settings, and contact and wearable sensors have been widely deployed. However, they are unsuitable for patients with burn wounds or infants with insufficient areas for attachment. Contactless detection can be achieved using camera imaging, but it is susceptible to ambient light conditions and has privacy concerns. Here we report a photonic radar for non-contact vital sign detection to overcome these challenges. This photonic radar can achieve millimetre-level range resolution based on synthesized radar signals with a bandwidth of up to 30 GHz. The high resolution of the radar system enables accurate respiratory detection from breathing simulators and a cane toad as a human proxy. Moreover, we demonstrate that the optical signals generated from the proposed system can enable vital sign detection based on light detection and ranging (LiDAR). This demonstration reveals the potential of a sensor-fusion architecture that can combine the complementary features of radar and LiDAR to achieve improved sensing accuracy and system resilience. The work provides a technical basis for contactless and high-resolution vital sign detection to meet the increasing demands of future medical and healthcare applications.

CEEMDAN-ICA-Based Radar Monitoring of Adjacent Multi-Target Vital Signs

In recent years, radar, especially frequency-modulated continuous wave (FMCW) radar, has been extensively used in non-contact vital signs (NCVS) research. However, current research does not work when multiple human targets are close to each other, especially when adjacent human targets lie in the same resolution cell. In this paper, a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–independent component analysis (ICA) was proposed to obtain the vital-sign information (including respiratory rate and heart rate) of adjacent human targets by using a single FMCW radar. Firstly, the data observed at a single angle were decomposed by the CEEMDAN separation algorithm to construct virtual multi-angle observations. It can effectively transform the undetermined blind source separation (UBSS) problem into an overdetermined blind source separation (BSS) problem. Thus, a BSS algorithm based on FastICA can be used to reconstruct each person’s vital-sign signal and then calculate their respiratory rate/heart rate. To validate the effectiveness of the proposed method, experiments based on the measured data were conducted and the results show that the proposed method can obtain multi-target vital-sign information even when they are in the same resolution cell.