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Sensors for Vital Signs Monitoring

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 27834

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Special Issue Editors

Prof. Dr. Jong-Ryul Yang

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Guest Editor

Department of Electrical and Electronics Engineering, Konkuk University, 120, Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea
Interests: RF/millimeter-wave transceiver front-end IC design for radar systems; terahertz-wave integrated circuits and systems; MMIC design; miniaturized radar sensors; CW/FSK/FMCW radar sensors; remote vital sign detection; HRV analysis using radar sensors
Special Issues, Collections and Topics in MDPI journals

Dr. Eugin Hyun

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Guest Editor

Division of Automotive Technology, DGIST, Daegu 42988, Korea
Interests: radar signal processing; target detection/tracking/classification; radar machine learning; human indication; automotive and smart city applications
Special Issues, Collections and Topics in MDPI journals

Dr. Sun Kwon Kim

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Guest Editor

Dept. Electro-medical Research Center, Korea Electro-Technology Research Institute, Korea
Interests: Medical Signal Processing & Machine Learning, Antenna Array Control using Machine Learning

Special Issue Information

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems as well as traditional medical purposes, such as disease indication judgment and prediction. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies cover contact sensors, such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiographm (BCG), and invasive/non-invasive sensors for diagnosis of the variation in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, and design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, a machine learning-based diagnostic technology can be used for extracting meaningful information from the continuous monitoring data. All the above can be included in the topics of the papers submitted to this Special Issue.

Prof. Dr. Jong-Ryul Yang
Dr. Eugin Hyun
Dr. Sun Kwon Kim
Guest Editors

Manuscript Submission Information

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Keywords

Novel and enhanced sensors of vital signs monitoring
Innovative vital signs sensing technologies and applications
Circuits and systems of miniaturized sensors for vital signs monitoring
New processing and analysis algorithms and machine learning for vital signs monitoring
Low-power wireless communication technologies for vital signs monitoring
Energy-efficient battery management and wireless power transmission for vital-sign sensors
Big data challenges and the Internet of Things for vital signs monitoring

Published Papers (7 papers)

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Research

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17 pages, 5974 KiB

Open AccessArticle

The Optimal Selection of Mother Wavelet Function and Decomposition Level for Denoising of DCG Signal

by Young In Jang, Jae Young Sim, Jong-Ryul Yang and Nam Kyu Kwon

Sensors 2021, 21(5), 1851; https://doi.org/10.3390/s21051851 - 6 Mar 2021

Cited by 42 | Viewed by 4583

Abstract

The aim of this paper is to find the optimal mother wavelet function and wavelet decomposition level when denoising the Doppler cardiogram (DCG), the heart signal obtained by the Doppler radar sensor system. To select the best suited mother wavelet function and wavelet decomposition level, this paper presents the quantitative analysis results. Both the optimal mother wavelet and decomposition level are selected by evaluating signal-to-noise-ratio (SNR) efficiency of the denoised signals obtained by using the wavelet thresholding method. A total of 115 potential functions from six wavelet families were examined for the selection of the optimal mother wavelet function and 10 levels (1 to 10) were evaluated for the choice of the best decomposition level. According to the experimental results, the most efficient selections of the mother wavelet function are “db9” and “sym9” from Daubechies and Symlets families, and the most suitable decomposition level for the used signal is seven. As the evaluation criterion in this study rates the efficiency of the denoising process, it was found that a mother wavelet function longer than 22 is excessive. The experiment also revealed that the decomposition level can be predictable based on the frequency features of the DCG signal. The proposed selection of the mother wavelet function and the decomposition level could reduce noise effectively so as to improve the quality of the DCG signal in information field. Full article

(This article belongs to the Special Issue Sensors for Vital Signs Monitoring)

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20 pages, 9022 KiB

Open AccessArticle

Machine Learning-Based Human Recognition Scheme Using a Doppler Radar Sensor for In-Vehicle Applications

by Eugin Hyun, Young-Seok Jin, Jae-Hyun Park and Jong-Ryul Yang

Sensors 2020, 20(21), 6202; https://doi.org/10.3390/s20216202 - 30 Oct 2020

Cited by 10 | Viewed by 4638

Abstract

In this paper, we propose a Doppler spectrum-based passenger detection scheme for a CW (Continuous Wave) radar sensor in vehicle applications. First, we design two new features, referred to as an ‘extended degree of scattering points’ and a ‘different degree of scattering points’ to represent the characteristics of the non-rigid motion of a moving human in a vehicle. We also design one newly defined feature referred to as the ‘presence of vital signs’, which is related to extracting the Doppler frequency of chest movements due to breathing. Additionally, we use a BDT (Binary Decision Tree) for machine learning during the training and test steps with these three extracted features. We used a 2.45 GHz CW radar front-end module with a single receive antenna and a real-time data acquisition module. Moreover, we built a test-bed with a structure similar to that of an actual vehicle interior. With the test-bed, we measured radar signals in various scenarios. We then repeatedly assessed the classification accuracy and classification error rate using the proposed algorithm with the BDT. We found an average classification accuracy rate of 98.6% for a human with or without motion. Full article

(This article belongs to the Special Issue Sensors for Vital Signs Monitoring)

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26 pages, 11588 KiB

Open AccessArticle

A Patient-Specific 3D+t Coronary Artery Motion Modeling Method Using Hierarchical Deformation with Electrocardiogram

by Siyeop Yoon, Changhwan Yoon, Eun Ju Chun and Deukhee Lee

Sensors 2020, 20(19), 5680; https://doi.org/10.3390/s20195680 - 5 Oct 2020

Cited by 1 | Viewed by 2133

Abstract

Cardiovascular-related diseases are one of the leading causes of death worldwide. An understanding of heart movement based on images plays a vital role in assisting postoperative procedures and processes. In particular, if shape information can be provided in real-time using electrocardiogram (ECG) signal information, the corresponding heart movement information can be used for cardiovascular analysis and imaging guides during surgery. In this paper, we propose a 3D+t cardiac coronary artery model which is rendered in real-time, according to the ECG signal, where hierarchical cage-based deformation modeling is used to generate the mesh deformation used during the procedure. We match the blood vessel’s lumen obtained from the ECG-gated 3D+t CT angiography taken at multiple cardiac phases, in order to derive the optimal deformation. Splines for 3D deformation control points are used to continuously represent the obtained deformation in the multi-view, according to the ECG signal. To verify the proposed method, we compare the manually segmented lumen and the results of the proposed method for eight patients. The average distance and dice coefficient between the two models were 0.543 mm and 0.735, respectively. The required time for registration of the 3D coronary artery model was 23.53 s/model. The rendering speed to derive the model, after generating the 3D+t model, was faster than 120 FPS. Full article

(This article belongs to the Special Issue Sensors for Vital Signs Monitoring)

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17 pages, 6700 KiB

Open AccessArticle

Vital-Signs Detector Based on Frequency-Shift Keying Radar

by Jae Young Sim, Jae-Hyun Park and Jong-Ryul Yang

Sensors 2020, 20(19), 5516; https://doi.org/10.3390/s20195516 - 26 Sep 2020

Cited by 9 | Viewed by 3161

Abstract

A frequency-shift keying (FSK) radar in the 2.45-GHz band is proposed for highly accurate vital-signs detection. The measurement accuracy of the proposed detector for the heartbeat is increased by using the cross-correlation between the phase differences of signals at two frequencies used by the FSK radar, which alternately transmits and receives the signals with different frequencies. Two frequencies—2.45 and 2.5 GHz—are effectively discriminated by using the envelope detection with the frequency control signal of the signal generator in the output waveform of the FSK radar. The phase difference between transmitted and received signals at each frequency is determined after calibrating the I / Q imbalance and direct-current offset using a data-based imbalance compensation algorithm, the Gram–Schmidt procedure, and the Pratt method. The absolute-distance measurement results for a human being show that the vital signs obtained at each frequency using the proposed FSK radar have a cross-correlation. The heartbeat detection results for the proposed FSK radar at a distance of < 2.4 m indicate a reduction in the error rate and an increase in the signal-to-noise ratio compared with those obtained using a single operating frequency. Full article

(This article belongs to the Special Issue Sensors for Vital Signs Monitoring)

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11 pages, 3012 KiB

Open AccessArticle

Frontal EEG Changes with the Recovery of Carotid Blood Flow in a Cardiac Arrest Swine Model

by Heejin Kim, Ki Hong Kim, Ki Jeong Hong, Yunseo Ku, Sang Do Shin and Hee Chan Kim

Sensors 2020, 20(11), 3052; https://doi.org/10.3390/s20113052 - 28 May 2020

Cited by 4 | Viewed by 2424

Abstract

Monitoring cerebral circulation during cardiopulmonary resuscitation (CPR) is essential to improve patients’ prognosis and quality of life. We assessed the feasibility of non-invasive electroencephalography (EEG) parameters as predictive factors of cerebral resuscitation in a ventricular fibrillation (VF) swine model. After 1 min untreated VF, four cycles of basic life support were performed and the first defibrillation was administered. Sustained return of spontaneous circulation (ROSC) was confirmed if a palpable pulse persisted for 20 min. Otherwise, one cycle of advanced cardiovascular life support (ACLS) and defibrillation were administered immediately. Successfully defibrillated animals were continuously monitored. If sustained ROSC was not achieved, another cycle of ACLS was administered. Non-ROSC was confirmed when sustained ROSC did not occur after 10 ACLS cycles. EEG and hemodynamic parameters were measured during experiments. Data measured for approximately 3 s right before the defibrillation attempts were analyzed to investigate the relationship between the recovery of carotid blood flow (CBF) and non-invasive EEG parameters, including time- and frequency-domain parameters and entropy indices. We found that time-domain magnitude and entropy measures of EEG correlated with the change of CBF. Further studies are warranted to evaluate these EEG parameters as potential markers of cerebral circulation during CPR. Full article

(This article belongs to the Special Issue Sensors for Vital Signs Monitoring)

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Review

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24 pages, 2669 KiB

Open AccessReview

Wearable Sensors Incorporating Compensatory Reserve Measurement for Advancing Physiological Monitoring in Critically Injured Trauma Patients

by Victor A. Convertino, Steven G. Schauer, Erik K. Weitzel, Sylvain Cardin, Mark E. Stackle, Michael J. Talley, Michael N. Sawka and Omer T. Inan

Sensors 2020, 20(22), 6413; https://doi.org/10.3390/s20226413 - 10 Nov 2020

Cited by 28 | Viewed by 3990

Abstract

Vital signs historically served as the primary method to triage patients and resources for trauma and emergency care, but have failed to provide clinically-meaningful predictive information about patient clinical status. In this review, a framework is presented that focuses on potential wearable sensor technologies that can harness necessary electronic physiological signal integration with a current state-of-the-art predictive machine-learning algorithm that provides early clinical assessment of hypovolemia status to impact patient outcome. The ability to study the physiology of hemorrhage using a human model of progressive central hypovolemia led to the development of a novel machine-learning algorithm known as the compensatory reserve measurement (CRM). Greater sensitivity, specificity, and diagnostic accuracy to detect hemorrhage and onset of decompensated shock has been demonstrated by the CRM when compared to all standard vital signs and hemodynamic variables. The development of CRM revealed that continuous measurements of changes in arterial waveform features represented the most integrated signal of physiological compensation for conditions of reduced systemic oxygen delivery. In this review, detailed analysis of sensor technologies that include photoplethysmography, tonometry, ultrasound-based blood pressure, and cardiogenic vibration are identified as potential candidates for harnessing arterial waveform analog features required for real-time calculation of CRM. The integration of wearable sensors with the CRM algorithm provides a potentially powerful medical monitoring advancement to save civilian and military lives in emergency medical settings. Full article

(This article belongs to the Special Issue Sensors for Vital Signs Monitoring)

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Other

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14 pages, 6414 KiB

Open AccessLetter

Effect of Filtered Back-Projection Filters to Low-Contrast Object Imaging in Ultra-High-Resolution (UHR) Cone-Beam Computed Tomography (CBCT)

by Sunghoon Choi, Chang-Woo Seo and Bo Kyung Cha

Sensors 2020, 20(22), 6416; https://doi.org/10.3390/s20226416 - 10 Nov 2020

Cited by 2 | Viewed by 3073

Abstract

In this study, the effect of filter schemes on several low-contrast materials was compared using standard and ultra-high-resolution (UHR) cone-beam computed tomography (CBCT) imaging. The performance of the UHR-CBCT was quantified by measuring the modulation transfer function (MTF) and the noise power spectrum (NPS). The MTF was measured at the radial location around the cylindrical phantom, whereas the NPS was measured in the eight different homogeneous regions of interest. Six different filter schemes were designed and implemented in the CT sinogram from each imaging configuration. The experimental results indicated that the filter with smaller smoothing window preserved the MTF up to the highest spatial frequency, but larger NPS. In addition, the UHR imaging protocol provided 1.77 times better spatial resolution than the standard acquisition by comparing the specific spatial frequency (f₅₀) under the same conditions. The f₅₀s with the flat-top window in UHR mode was 1.86, 0.94, 2.52, 2.05, and 1.86 lp/mm for Polyethylene (Material 1, M1), Polystyrene (M2), Nylon (M3), Acrylic (M4), and Polycarbonate (M5), respectively. The smoothing window in the UHR protocol showed a clearer performance in the MTF according to the low-contrast objects, showing agreement with the relative contrast of materials in order of M3, M4, M1, M5, and M2. In conclusion, although the UHR-CBCT showed the disadvantages of acquisition time and radiation dose, it could provide greater spatial resolution with smaller noise property compared to standard imaging; moreover, the optimal window function should be considered in advance for the best UHR performance. Full article

(This article belongs to the Special Issue Sensors for Vital Signs Monitoring)

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