Vital Sign Measurements Research Articles

Remote Vital Signs Measurement of Indoor Walking Persons Using mm-Wave FMCW Radar

Research on radar-based non-contact vital sign monitoring systems is critical during the COVID-19 epidemic. The accuracy of remote vital sign measurements has increased with the advancement of radar technology and various algorithms. Most studies require subjects to remain stationary, such as standing, sitting in a chair, or lying on a bed, and various measurement algorithms have been proposed. However, maintaining a stationary state as a prerequisite for measurement limits the development and application prospects of radar-based vital sign monitoring systems. Therefore, this paper presents a novel method for monitoring the vital signs of moving targets using a millimeter-wave frequency-modulated continuous-wave (FMCW) radar. The experimental results showed that regardless of whether the subjects walked at 1 m/s or with the left side of their body facing the radar, the accuracy of the heart rate measurement remained high. In the fixed-route experiments, the root mean squared error (RMSE) for heart rate estimation was 4.09 bpm, with an accuracy of 95.88%.

Mutual Interference Suppression Using Signal Separation and Adaptive Mode Decomposition in Noncontact Vital Sign Measurements

Noncontact vital sign measurements based on millimeter-wave radar can realize long-range detection of respiratory and heartbeat signals, therefore it is gradually applied in more and more scenes. With an increase in the number of millimeter-wave radar devices, the mutual interference between radar signals will occur, which will increase the noise floor. Moreover, respiratory and heartbeat signals are relatively weak and easy to be submerged by the high noise floor. To solve this problem, we present a novel method for suppressing mutual interference and extracting vital signs using improved morphological component analysis (IMCA) and an adaptive parameter optimization variational mode decomposition (APVMD) algorithm. The IMCA algorithm is used to suppress the mutual interference based on the different sparsities of components in different sparse domains, and this improvement solves the inability of the MCA algorithm to detect the interfered signal. Then, the APVMD algorithm is used to extract respiratory and heartbeat signals from the signal containing residual noise. The rate of energy loss is used as the index to optimize the parameters, and the step size of the penalty value is adaptively adjusted according to the center frequency of the mode. The simulation and field experiment results show that the proposed method can significantly improve the signal-to-noise ratios (SNRs) of respiratory and heartbeat signals in the case of mutual interference between radar signals. Moreover, a comparison with other interference suppression and extraction methods shows that the proposed method better improves the SNR and accuracy.

Accuracy and reliability of a wireless vital signs monitor for hospitalized patients in a low-resource setting.

ObjectiveThe purpose of this study was to evaluate the accuracy and reliability of neoGuard in comparison to a conventional bedside monitor on patients in a low-resource clinical setting.DesignThis was a single-arm methods comparison study involving the use of a wearable vital signs monitor (neoGuardTM) versus a conventional bedside monitor (Edan iM8).SettingThe study was conducted at Jinja Regional Referral Hospital, a tertiary care hospital situated in Eastern Uganda.ParticipantsThirty patients (10 male, 20 female) were enrolled from the adult recovery ward at JRRH. Participants were eligible for the study if they were at least 18 years of age, had 2 sets of normal vital sign measurements obtained 1 h apart, and were able and willing to provide informed consent.Main Outcome and MeasuresThe primary outcome measures were (i) bias (mean deviation) and (ii) limits of agreement [95% CI]. Bland-Altman plots were generated to illustrate the level of agreement between the neoGuardTM technology and the Edan iM8 monitor.ResultsBland-Altman analysis was performed for 24 participants; datasets from six participants were excluded due to missing or invalid measurements. Findings showed a moderate level of agreement for measurement of SpO2, PR, and RR, with >80% of subject means falling within the predefined acceptability limits. However, there was also notable variation in accuracy between subjects, with large standard deviations observed for measurement of all four parameters. While the level of agreement for measurement of temperature was low, this is partly explained by limitations in the comparison method.

Neuronal Hyperexcitability: The Elusive But Modifiable Instigator of Disease

Despite enormous strides in medical diagnostics and the ability to analyze and track disease processes, the underlying cause of most psychiatric and medical disorders remains unclear. Consequently, the treatment of these disorders continues to be more palliative than preventive. However, an emerging hypothesis contends that severe and persistent stress is at the root of most psychiatric and general medical conditions. Although this idea is not new, what is new is the identification of a powerful but elusive endogenous driver of the stress. According to the Multi-Circuit Neuronal Hyperexcitability (MCNH) hypothesis, ordinary daily stressors are abnormally amplified by an inherent hyperexcitability of the neurological system. This pathophysiological trait, which appears to be inherited in an autosomal dominant distribution and expressed through the central and peripheral nervous systems, causes shockwaves to be repeatedly sent through the body, keeping it in fight-or-flight mode much or all of the time. The strain that this commonly-occurring trait places on various organs and systems of the body can cause repeated bouts of mental illness and a gradual progression of physical illnesses such as diabetes mellitus, high blood pressure, cardiovascular disease, autoimmune diseases, cancer, and dementia. Although the etiology of most illnesses is thought to be multifactorial, the genealogical distribution in which they occur and the relationship that they have to one another strongly suggests that among the various psychological, biological, and environmental factors that contribute to their development, the trait of neuronal hyperexcitability is the most important. This observation has enormous implications because neuronal hyperexcitability is a highly modifiable risk factor. Any psychological, behavioral, dietary, or medical intervention that quiets the nervous system can potentially prevent or delay the development of the various illnesses that are thought to be fueled by the neuronal hyperexcitability trait. In addition, there is emerging evidence that the trait can be detected through resting vital-sign measurements. The ease of this objective measurement and the power that it has to incentivize affected persons to take control of their health underscores the importance of recognizing the connection between neuronal hyperexcitability and the pathogenesis of disease.

Feasibility Assessment of Using Smart Trackers in Telemedicine Systems to Remotely Monitor the Overall Health of Patients in Real-Time

Introduction. The development of telemedicine systems intended to remotely monitor the health status of patients constitutes one of the most promising areas of contemporary science. Particularly relevant is the use of such systems to track the health of surgically treated patients. Since such patients are mobile, sensors measuring vital signs should not cause any discomfort in regular use. Thus, wearable smart trackers can be used for these purposes.Aim. The paper aims to study the possibility of using smart trackers in a telemedicine system for monitoring patient health.Materials and methods. In the study, a series of measurements was performed using GSMIN WR 41 smart bracelets in order to estimate their accuracy when measuring pulse, blood pressure, and oxygen saturation for different research subjects at rest and during intense physical exercises. In addition, the average measurement interval of the smart bracelet was determined; the operation of the smart bracelet in the telemedicine system for monitoring vital signs was considered.Results. The studies show that an average error in the measurement of vital signs (except for systolic pressure) using the smart bracelet does not exceed 10 %. In order to avoid the high error in measuring systolic pressure, an algorithm for assessing the general health of patients was developed. In addition, it is shown that the smart bracelet can detect an abrupt change in the vital signs of patients, with the average time of their measurement and data transmission to the server of the telemedicine system coming to 45 seconds.Conclusion. The study results indicate that smart bracelets can be used to remotely monitor the health of patients in real-time. These findings are confirmed by the telemedicine system designed on the basis of the smart bracelets considered in the paper.

Orthostatic Hypotension and Antiparkinsonian Drugs: A Systematic Review and Meta-analysis.

BackgroundOrthostatic hypotension (OH) is multifactorial in Parkinson’s disease (PD). Antiparkinsonian medication can contribute to OH, leading to increased risk of falls, weakness and fatigue.MethodsWe conducted a systematic review and meta-analysis of randomised controlled trials (RCTs) of antiparkinsonian drugs associated with OH as an adverse effect, compared to placebo. We searched EMBASE, MEDLINE and Web of Science databases until November 2020. Analysis used fixed-effects models and the GRADE tool to rate quality of evidence. Meta-analysis was performed if 3 or more studies of a drug group were available.ResultsTwenty-one RCTs including 3783 patients were included comparing 6 PD drug groups to placebo (MAO-B inhibitors, dopamine agonists, levodopa, COMT inhibitors, levodopa and adenosine receptor antagonists). OH was recorded as an adverse event or measurement of vital signs, without further specification on how this was defined or operationalised. Meta-analysis was performed for MAO-B inhibitors and dopamine agonists, as there were 3 or more studies for these drug groups. In this analysis, compared with placebo, neither MAO-B inhibitors or dopamine agonists were associated with increased risk of OH, (OR 2.28 [95% CI:0.81–6.46]), (OR 1.39 [95% CI:0.97–1.98]).ConclusionsMost studies did not specifically report OH, or reporting of OH was limited, including how and when it was measured. Furthermore, studies specifically reporting OH included participants that were younger than typical PD populations without multimorbidity. Future trials should address this, for example,, by including individuals over the age of 75, to improve estimations of how antiparkinsonian medications affect risk of OH.

Evaluation of a digital system to predict unplanned admissions to the intensive care unit: A mixed-methods approach

BackgroundWe have developed the Hospital Alerting Via Electronic Noticeboard (HAVEN) which aims to identify hospitalised patients most at risk of reversible deterioration. HAVEN combines patients’ vital-sign measurements with laboratory results, demographics and comorbidities using a machine learnt algorithm. ObjectivesThe aim of this study was to identify variables or concepts that could improve HAVEN predictive performance. MethodsThis was an embedded, mixed methods study. Eligible patients with the five highest HAVEN scores in the hospital (i.e., ‘HAVEN Top 5′) had their medical identification details recorded. We conducted a structured medical note review on these patients 48 hours post their identifiers being recorded. Methods of constant comparison were used during data collection and to analyse patient data. ResultsThe 129 patients not admitted to ICU then underwent constant comparison review, which produced three main groups. Group 1 were patients referred to specialist services (n = 37). Group 2 responded to ward-based treatment, (n = 38). Group 3 were frail and had documented treatment limitations (n = 47). ConclusionsDigital-only validation methods code the cohort not admitted to ICU as ‘falsely positive’ in sensitivity analyses however this approach limits the evaluation of model performance. Our study suggested that coding for patients referred to other specialist teams, those with treatment limitations in place, along with those who are deteriorating but then respond to ward-based therapies, would give a more accurate measure of the value of the scores, especially in relation to cost-effectiveness of resource utilisation.

Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.

ObjectiveTo determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis.Materials and methodsPubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted.ResultsThe clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is often combined with common structured data such as demographics, vital signs, laboratory data, and medications. Area under the receiver operating characteristic curve (AUC) comparison of ML methods showed that utilizing both text and structured data predicts sepsis earlier and more accurately than structured data alone. No meta-analysis was performed because of incomparable measurements among the 9 included studies.DiscussionStudies focused on sepsis identification or early detection before onset; no studies used patient histories beyond the current episode of care to predict sepsis. Sepsis definition affects reporting methods, outcomes, and results. Many methods rely on continuous vital sign measurements in intensive care, making them not easily transferable to general ward units.ConclusionsApproaches were heterogeneous, but studies showed that utilizing both unstructured text and structured data in ML can improve identification and early detection of sepsis.

Compression and Encryption of Vital Signals Using an SoC-FPGA

This article presents the implementation of a remote monitoring system of biomedical signals with cybersecurity support and compression of vital sign signals and data of the patient. This system uses a low-cost microsystem for encrypting and compressing the information using the Lempel–Ziv–Welch (LZW) lossless compression algorithm and the Advanced Encryption Standard (AES). In this case, WolfSSL library is used for the implementation of the Transport Layer Security (TLS) protocol, whose encryption function is accelerated by the AES processor designed on a System on Chip - Field Programmable Gate Array (SoC-FPGA) device. A multiparameter board and an SoC-FPGA development board make up the vital signs measurement system, which was calibrated and verified by a commercial patient simulator. Data transmission tests were carried out from the measurement system to the monitoring application developed in LabVIEW and implemented on a personal computer (PC), where vital signs and data of the patient are decrypted and decompressed. Also, it was possible to verify a significant improvement in the performance of the TLS connection. From the results obtained, it can be concluded that the designed microsystem allows to compress, encrypt, and transmit biomedical data in real-time and without loss of information. The microsystem is very suitable for e-health platforms and/or e-health devices that use unsecured communication networks with limited bandwidth.

Accurate Measurement of Human Vital Signs With Linear FMCW Radars Under Proximity Stationary Clutters.

Vital sign detection using linear frequency-modulated continuous-wave (LFMCW) radar may be subject to the proximity stationary clutters. This paper presents a novel technique to synthesize the slow-time I/Q signals, which are equivalent to those in a single tone quadrature CW radar, from a single-channel LFMCW radar. It correlates the two types of radars in such a way that the proximity stationary clutters are translated to direct current (DC) offsets in the synthesized I/Q signals across slow-time. The circle-fitting based DC offsets calibration (DCcal) technique, which was developed for CW radar, can now be applied to eliminate the impact of the proximity stationary clutters in LFMCW radars for accurate vital sign detection. Moreover, the modified differentiate and cross-multiply (MDACM) algorithm can also be leveraged to eliminate the phase ambiguity issue. Thorough theoretical analysis and working principles are presented. Simulations are performed to validate the proposed technique. Moreover, exhaustive experiments are carried out with a millimeter-wave 79 GHz FMCW radar in the office environment. Mechanical vibration and vital signs are extracted with micrometer-level accuracy in the existence of proximity stationary clutters.

Toward closed-loop drug delivery: Integrating wearable technologies with transdermal drug delivery systems.

The recent advancement and prevalence of wearable technologies and their ability to make digital measurements of vital signs and wellness parameters have triggered a new paradigm in the management of diseases. Drug delivery as a function of stimuli or response from wearable, closed-loop systems can offer real-time on-demand or preprogrammed drug delivery capability and offer total management of disease states. Here we review the key opportunities in this space for development of closed-loop systems, given the advent of digital wearable technologies. Particular considerations and focus are given to closed-loop systems combined with transdermal drug delivery technologies.

Assessment and measurement of vital signs of human beings using IoT architecture

Assessment and measurement of vital signs of human beings using IoT architecture

The change over time of vital signs with consideration for opioid use in the last 2weeks of life among cancer patients in a palliative care unit: Continuous measurement of vital signs using a non-wearable monitor.

ObjectivesThe aim of this study was to examine the following by using a non‐wearable monitor: (ⅰ) the trajectory of vital signs (VS) in the last 2 weeks of life among cancer patients, and (ⅱ) the difference in change over time of VS between cancer patients with and without opioid use.MethodsWe conducted a longitudinal study involving cancer patients in a palliative care unit (PCU) from April 2018 to October 2019. VS were collected continuously using a non‐wearable monitor, and we calculated the means of respiratory rate (RR) and heart rate (HR) per hour, and counts of apnea per hour as outcome variables. Explanatory variables were time (subtracting time of death from measurement time of VS, divided by 36) and opioid use. Mean difference (MD) of time represented the slope per hour of VS values. First, we analyzed the associations between VS per hour and time using a linear mixed model (LMM) with random intercepts and slope over time. Second, we analyzed the associations between VS and interaction term between time and opioid use.ResultsWe analyzed 26 cancer patients. RR (MD: 0.27 beats/min [95% CI: 0.27–0.28]), HR (MD: 1.51 beats/min [95% CI: 1.50–1.52]), and apnea (MD: 0.71 count/hour [95% CI: 0.70–0.72]) significantly increased hourly. In addition, RR was significantly associated with interaction term (MD: −1.59 beats/min [95% CI: −3.11 to −0.07]), which indicates that there is a difference in the slope of RR between opioid users and non‐users.ConclusionsWe have successfully described the trajectory of VS in high‐resolution under conditions of a natural end of life in PCU.

Tracking the National Early Warning Score 2 from prehospital care to the emergency department: A prospective, ambulance-based, observational study

Aim of the study: To assess the prognostic ability of the National Early Warning Score 2 (NEWS2) at three time points of care -at the emergency scene (NEWS2-1), just before starting the transfer by ambulance to the hospital (NEWS2- 2), and at the hospital triage box (NEWS2-3)- to estimate in-hospital mortality after two days since the index event. Methods: Prospective, multicenter, ambulance-based, cohort ongoing study in adults (>18 years) consecutively attended by advanced life support (ALS) and evacuated with high-priority to the emergency departments (ED) between October 2018 and May 2021. Vital sign measures were used to calculate the NEWS2 score at each time point, then this score was entered in a logistic regression model as the single predictor. Two outcomes were considered: first, all-cause mortality of the patients within 2 days of presentation to EMS, and second, unplanned ICU admission. The calibration and scores comparison was performed by representing the predicted vs the observed risk curves according to NEWS score value. Results: 4943 patients were enrolled. Median age was 69 years (interquartile range 53- 81). The NEWS2-3 presented the better performance for all-cause two-day in-hospital mortality with an AUC of 0.941 (95% CI: 0.917-0.964), showing statistical differences with both the NEWS2-1 (0.872 (95% CI: 0.833-0.911); p < 0.003) and with the NEWS2- 2 (0.895 (95% CI: 0.866-0.925; p < 0.05). The calibration and scores comparison results showed that the NEWS2-3 was the best predictive score followed by the NEWS2-2 and the NEWS2-1, respectively. Conclusions: The NEWS2 has an excellent predictive performance. The score showed a very consistent response over time with the difference between “at the emergency scene” and “pre-evacuation” presenting the sharpest change with decreased threshold values, thus displaying a drop in the risk of acute clinical impairment.

Abstract 14304: Incident Dialysis and Acute Kidney Injury Among Medicare Beneficiaries After Hospitalization for Heart Failure in the US

Background: Recent therapies in heart failure (HF) have been shown to slow kidney disease progression, yet the trajectory of clinically relevant kidney events in real-world HF care is not well described. Methods: We identified Medicare beneficiaries hospitalized for HF at 240 US sites in the GWTG-HF registry in 2016 who did not require dialysis prior to or during hospitalization and followed them through 1-year post-discharge for incident kidney events, all-cause and HF readmissions, and death. Associations between discharge CKD-EPI-derived eGFR and time-to-first events were modeled adjusting for demographics, medical history, LVEF, discharge vital sign and laboratory measures, and hospital characteristics. Results: Among 20,927 Medicare beneficiaries (age 81±9y; 53% women; 10% Black), discharge eGFR was ≥90, 60-89, 45-59, 30-44, and &lt;30 mL/min/1.73 m 2 in 4%, 27%, 23%, 25%, and 21%, respectively. By 1 year, 37% had died and 65% were readmitted (including 31% for HF, 7% for AKI, and 5% for dialysis/end-stage kidney disease [ESKD]). Lower discharge eGFR (per 10 mL/min/1.73 m 2 decrease) was independently associated with increased mortality (aHR 1.08[1.05-1.10]), all-cause readmission (aHR 1.04[1.02-1.06]), HF readmission (aHR 1.09[1.06-1.12]), readmission for AKI (aHR 1.20[1.15-1.25]), and progression to dialysis/ESKD (aHR 2.22 [1.93-2.55]); all P&lt;0.001. Categorical analyses consistently demonstrated heightened risks of HF and kidney events at lower eGFR. A potential J-shaped relationship was observed for mortality &amp; dialysis/ESKD. Conclusions: Older adults hospitalized for HF face broad post-discharge risks of recurrent HF events, kidney events, and death. One in 20 Medicare beneficiaries are estimated to progress to dialysis/ESKD within 1-year of hospital discharge. These data may inform shared decision-making and highlight the need to prioritize comprehensive HF care that simultaneously improve outcomes in diverse disease pathways.

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

Simultaneous multitarget vital signs measurement has become a hot issue for noncontact vital signs perception. However, there is still challenge in the multitarget heartbeat measurement due to the weakness of heartbeat signal and interference from complex environment. In this article, a new multiple-input–multiple-output (MIMO) continuous-wave (CW) radar system equipped with 2-D digital beamforming (DBF) is presented to measure the respiration and heartbeat of multiple human subjects at unknown positions simultaneously. Through 2-D beam scanning of the whole scene, a 2-D radar image is generated. From the image, the chest motion of multiple targets is accurately located. Then, the vital signs of targets are obtained through forming individual beams focusing on the chests of targets. Moreover, the low intermediate frequency (low-IF) architecture is adopted to minimize the impact of flicker noise in low-frequency amplifier stages. The experimental results demonstrate that the proposed MIMO 2-D imaging radar system can locate chest areas of multiple targets, suppress the clutters, and make vital signs measurement, heartbeat measurement in particular, more robust compared with single-input–multiple-output (SIMO) radar system in complex environment.

Contactless video-based photoplethysmography technique comparison investigating pulse transit time estimation of arterial blood pressure.

Non-contact measurement of physiological vital signs, such as blood pressure (BP), by video-based photoplethysmography (vPPG) is a potential means for remote health monitoring. However, the signal-to-noise ratio of cardiovascular signals within the vPPG is very low. This study investigates the potential of BP estimation from vPPG. In 10 healthy volunteers (4 females, 28 ± 7 years), continuous electrocardiogram, finger BP and video of the face and palm of the hand were recorded. BP was varied by isometric hand grip exercise and leg ischemia. Four vPPG methods were compared: (i) averages of the green (GREEN) color intensity; (ii) the best linear combination of color channels using independent component analysis (ICA); (iii) a linear combination of chrominance-based (CHROM) signal by standardizing the skin color profile; (iv) plane orthogonal to the skin tone (POS) as vPPG signal. These were applied to 14 regions of interest (ROIs) on the face and 5 ROIs on the palm. Pulse transit time (PTT) between ROIs, for all permutations, were calculated and the correlation with BP quantified. A significant, negative PTT-BP correlation was defined as success. A maximum success rate of 80% was achieved, occurring for the GREEN, POS and ICA methods only for specific ROIs within the face, but not for any permutation using the hand. These results indicate that the use of vPPG for estimation of BP will be challenging. A combination of different vPPG methods and within-face ROIs may yield useful information.

Proof-of-principle Experiment on 24 GHz Medical Radar for Non-contact Vital Signs Measurement.

Medical radar for non-contact vital signs measurement exhibits great potential in both clinical and home healthcare settings. Especially during the corona virus spreading time, non-contact sensing more clearly shows the advantages. Many previous studies have concentrated on medical radar-based healthcare applications, but pay less attention to the working principles. A clear understanding of medical radars at both the mathematical and physical levels is critically important for developing application-specific signal processing algorithms. Therefore, this study aims to re-define the operating principle of radar, and a proof-of-principle experiment was performed on both actuator and human subjects using 24 GHz Doppler radar system. Experimental results indicate that there is a difference in the radar output signals between the two cases, where the displacement is greater than and less than half of the wavelength. For the former situation, the displacement x = n.λ/2 (n ≥ 1), one peak of radar signals corresponds to n peaks of baseband signals. By contrast, for the latter situation, the displacement x < λ/2, one peak of radar signals corresponds to one peak of baseband signals. Strikingly, with human measurement on the dorsal side, the the number of respiration peaks are seen from the radar raw signals.

Design and Implementation of the Temperature Sensor for Health Care Monitoring Based on Optical Fiber Technology

Human health is represented due to the measurement of vital signs. The basic vital signs are temperature, heart pulse rate, oxygen percentage in blood, blood pressure, etc. These signs are changes according to the physical and mental status of the individual. So measuring and monitoring those signs are very important. In this work, design and implementation of human Temperature Sensor are submitted. This is achieved using optical fiber technology. Two sensor diameters were tested; 125 µm and 60µm. The obtained results show a shifting in wavelength towards the red region due to temperature application. The submitted sensor has good sensitivity and linearity. Both sensors exhibit good responsivity, sensitivity, and high linearity. The sensitivity is increased about five times when the diameter is decreased.

Multi-target vital signs detection using frequency-modulated continuous wave radar

Respiration and heartbeats rates are important physiological assessment indicators that provide valid prior-knowledge for the diagnosis of numerous diseases. However, most of the current research focuses on the vital signs measurement of single target, and multi-target vital signs detection has not received much attention. In this paper, we use frequency-modulated continuous wave (FMCW) radar to measure the vital signs signals of multi-target. First, we apply the three-dimensional fast Fourier transform (3D-FFT) method to separate multiple targets and get their distance and azimuth information. Subsequently, the linear constrained minimum variance-based adaptive beamforming (LCMV-ADBF) technique is proposed to form a spatially distributed beams on the targets of interest directions. Finally, a compressive sensing based on orthogonal matching pursuit (CS-OMP) method and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) method are present to extract the respiratory and heartbeat signals. We perform tests in a real experimental environment and compare the proposed method with reference devices. The results show that the degrees of agreement for respiratory and heartbeat are 89% and 87%, respectively, for two human targets. The level of agreement for respiratory and heartbeat is 87% and 85%, respectively, for three human targets, proving the effectiveness of the proposed method.