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Special Issue "Sensors for Biosignal Processing"

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

Deadline for manuscript submissions: closed (30 September 2018).

Special Issue Editors

Prof. Dr. Ki H. Chon
E-Mail Website
Guest Editor
Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: medical instrumentation, mobile health diagnostics, wearable sensors, biosignal processing, modeling, simulation, and development of novel algorithms to understand dynamic processes and extract distinct features of physiological systems
Special Issues and Collections in MDPI journals
Prof. Dr. Jo Woon Chong
E-Mail Website
Guest Editor
Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
Interests: biomedical signal processing; non-invasive physiological monitoring; sensors and wireless communication for medical care; machine learning techniques for healthcare data; home monitoring of health and disease
Prof. Dr. Bersaín A. Reyes
E-Mail Website
Guest Editor
Biomedical Engineering, Universidad Autónoma de San Luis Potosí, SLP 78290, Mexico
Interests: medical instrumentation; mobile health diagnostics; wearable sensors; biosignal processing; modeling, simulation, and development of novel algorithms to understand dynamic processes and extract distinct features of physiological systems

Special Issue Information

Dear Colleagues,

Wearable biomedical sensors—included in devices like smartwatches, smartphones, and smart textiles—have become so ubiquitous that they are increasingly being used as wearable health monitors. By taking advantage of a smart device’s processing power, peripheral noninvasive and cost-effective sensors, and wireless communications capabilities, recent efforts have been made to create various medical applications for self-monitoring. For example, there have been recent advances that allow respiratory rate measurement and cardiac arrhythmia detection, all directly from the video camera of a smartphone without the use of external sensors. More recent advances in smartwatches and smart textiles have allowed useful continuous health monitoring, including of heart rates, respiratory rates, and cardiac arrhythmias including atrial fibrillation. Given the progress to date, this Special Issue aims to publish new advances in biosignal processing algorithms for extracting physiological measurements from smart devices with or without external sensors. Some of the physiological capabilities of interest may include new advances in detection of heart rates, respiratory rates, tidal volume, respiratory sound diagnostics, cardiac arrhythmias, and blood pressure data acquisition and processing. New algorithm development for removing motion artifacts of the above-mentioned physiological measurements that can be applicable for smart wearable devices is also of significant interest for this Special Issue.

Prof. Dr. Ki H. Chon
Prof. Dr. Jo Woon Chong
Prof. Dr. Bersaín A. Reyes
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biosignal processing
  • wearable devices
  • biosensors
  • physiological measurements
  • vital sign
  • health monitoring
  • smartwatch
  • smartphone
  • smart textiles
  • smart devices
  • motion artifacts and machine learning

Published Papers (12 papers)

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Research

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Open AccessArticle
Automated Recognition of Epileptic EEG States Using a Combination of Symlet Wavelet Processing, Gradient Boosting Machine, and Grid Search Optimizer
Sensors 2019, 19(2), 219; https://doi.org/10.3390/s19020219 - 09 Jan 2019
Cited by 4
Abstract
Automatic recognition methods for non-stationary electroencephalogram (EEG) data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-class [...] Read more.
Automatic recognition methods for non-stationary electroencephalogram (EEG) data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-class classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets are adopted to decompose the EEG data into five frequencies sub-bands, such as gamma, beta, alpha, theta, and delta, whose statistical features were computed and used as classification features. The grid search optimizer is used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a conventional support vector machine and a random forest classifier constructed according to previous descriptions. Multiple performance indices were used to evaluate the proposed classification scheme, which provided better classification accuracy and detection effectiveness than has been recently reported in other studies on three-class classification of EEG data. Full article
(This article belongs to the Special Issue Sensors for Biosignal Processing)
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Open AccessArticle
Design of User-Customized Negative Emotion Classifier Based on Feature Selection Using Physiological Signal Sensors
Sensors 2018, 18(12), 4253; https://doi.org/10.3390/s18124253 - 03 Dec 2018
Cited by 1
Abstract
First, the Likert scale and self-assessment manikin are used to provide emotion analogies, but they have limits for reflecting subjective factors. To solve this problem, we use physiological signals that show objective responses from cognitive status. The physiological signals used are electrocardiogram, skin [...] Read more.
First, the Likert scale and self-assessment manikin are used to provide emotion analogies, but they have limits for reflecting subjective factors. To solve this problem, we use physiological signals that show objective responses from cognitive status. The physiological signals used are electrocardiogram, skin temperature, and electrodermal activity (EDA). Second, the degree of emotion felt, and the related physiological signals, vary according to the individual. KLD calculates the difference in probability distribution shape patterns between two classes. Therefore, it is possible to analyze the relationship between physiological signals and emotion. As the result, features from EDA are important for distinguishing negative emotion in all subjects. In addition, the proposed feature selection algorithm showed an average accuracy of 92.5% and made it possible to improve the accuracy of negative emotion recognition. Full article
(This article belongs to the Special Issue Sensors for Biosignal Processing)
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Open AccessArticle
A Smartphone-Based System for Automated Bedside Detection of Crackle Sounds in Diffuse Interstitial Pneumonia Patients
Sensors 2018, 18(11), 3813; https://doi.org/10.3390/s18113813 - 07 Nov 2018
Abstract
In this work, we present a mobile health system for the automated detection of crackle sounds comprised by an acoustical sensor, a smartphone device, and a mobile application (app) implemented in Android. Although pulmonary auscultation with traditional stethoscopes had been used for decades, [...] Read more.
In this work, we present a mobile health system for the automated detection of crackle sounds comprised by an acoustical sensor, a smartphone device, and a mobile application (app) implemented in Android. Although pulmonary auscultation with traditional stethoscopes had been used for decades, it has limitations for detecting discontinuous adventitious respiratory sounds (crackles) that commonly occur in respiratory diseases. The proposed app allows the physician to record, store, reproduce, and analyze respiratory sounds directly on the smartphone. Furthermore, the algorithm for crackle detection was based on a time-varying autoregressive modeling. The performance of the automated detector was analyzed using: (1) synthetic fine and coarse crackle sounds randomly inserted to the basal respiratory sounds acquired from healthy subjects with different signal to noise ratios, and (2) real bedside acquired respiratory sounds from patients with interstitial diffuse pneumonia. In simulated scenarios, for fine crackles, an accuracy ranging from 84.86% to 89.16%, a sensitivity ranging from 93.45% to 97.65%, and a specificity ranging from 99.82% to 99.84% were found. The detection of coarse crackles was found to be a more challenging task in the simulated scenarios. In the case of real data, the results show the feasibility of using the developed mobile health system in clinical no controlled environment to help the expert in evaluating the pulmonary state of a subject. Full article
(This article belongs to the Special Issue Sensors for Biosignal Processing)
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Open AccessArticle
Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions
Sensors 2018, 18(11), 3705; https://doi.org/10.3390/s18113705 - 31 Oct 2018
Abstract
Respiratory rate (RR) is a key parameter used in healthcare for monitoring and predicting patient deterioration. However, continuous and automatic estimation of this parameter from wearable sensors is still a challenging task. Various methods have been proposed to estimate RR from wearable sensors [...] Read more.
Respiratory rate (RR) is a key parameter used in healthcare for monitoring and predicting patient deterioration. However, continuous and automatic estimation of this parameter from wearable sensors is still a challenging task. Various methods have been proposed to estimate RR from wearable sensors using windowed segments of the data; e.g., often using a minimum of 32 s. Little research has been reported in the literature concerning the instantaneous detection of respiratory rate from such sources. In this paper, we develop and evaluate a method to estimate instantaneous respiratory rate (IRR) from body-worn reflectance photoplethysmography (PPG) sensors. The proposed method relies on a nonlinear time-frequency representation, termed the wavelet synchrosqueezed transform (WSST). We apply the latter to derived modulations of the PPG that arise from the act of breathing.We validate the proposed algorithm using (i) a custom device with a PPG probe placed on various body positions and (ii) a commercial wrist-worn device (WaveletHealth Inc., Mountain View, CA, USA). Comparator reference data were obtained via a thermocouple placed under the nostrils, providing ground-truth information concerning respiration cycles. Tracking instantaneous frequencies was performed in the joint time-frequency spectrum of the (4 Hz re-sampled) respiratory-induced modulation using the WSST, from data obtained from 10 healthy subjects. The estimated instantaneous respiratory rates have shown to be highly correlated with breath-by-breath variations derived from the reference signals. The proposed method produced more accurate results compared to averaged RR obtained using 32 s windows investigated with overlap between successive windows of (i) zero and (ii) 28 s. For a set of five healthy subjects, the averaged similarity between reference RR and instantaneous RR, given by the longest common subsequence (LCSS) algorithm, was calculated as 0.69; this compares with averaged similarity of 0.49 using 32 s windows with 28 s overlap between successive windows. The results provide insight into estimation of IRR and show that upper body positions produced PPG signals from which a better respiration signal was extracted than for other body locations. Full article
(This article belongs to the Special Issue Sensors for Biosignal Processing)
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Open AccessArticle
New Method for Pure-Tone Audiometry Using Electrooculogram: A Proof-of-Concept Study
Sensors 2018, 18(11), 3651; https://doi.org/10.3390/s18113651 - 28 Oct 2018
Abstract
Precise and timely evaluation of an individual’s hearing loss plays an important role in determining appropriate treatment strategies, including medication and aural rehabilitation. However, currently available hearing assessment systems do not satisfy the need for an objective assessment tool with a simple and [...] Read more.
Precise and timely evaluation of an individual’s hearing loss plays an important role in determining appropriate treatment strategies, including medication and aural rehabilitation. However, currently available hearing assessment systems do not satisfy the need for an objective assessment tool with a simple and non-invasive procedure. In this paper, we propose a new method for pure-tone audiometry, which may potentially be used to assess an individual’s hearing ability objectively and quantitatively, without need for the user’s active response. The proposed method is based on the auditory oculogyric reflex, where the eyes involuntary rotate towards the source of a sound, in response to spatially moving pure-tone audio stimuli modulated at specific frequencies and intensities. We quantitatively analyzed horizontal electrooculograms (EOG) recorded with a pair of electrodes under two conditions—when pure-tone stimuli were (1) “inaudible” or (2) “audible” to a participant. Preliminary experimental results showed significantly increased EOG amplitude in the audible condition compared to the inaudible condition for all ten healthy participants. This demonstrates potential use of the proposed method as a new non-invasive hearing assessment tool. Full article
(This article belongs to the Special Issue Sensors for Biosignal Processing)
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Open AccessArticle
Pulse Oximetry with Two Infrared Wavelengths without Calibration in Extracted Arterial Blood
Sensors 2018, 18(10), 3457; https://doi.org/10.3390/s18103457 - 15 Oct 2018
Abstract
Oxygen saturation in arterial blood (SaO2) provides information about the performance of the respiratory system. Non-invasive measurement of SaO2 by commercial pulse oximeters (SpO2) make use of photoplethysmographic pulses in the red and infrared regions and utilizes the [...] Read more.
Oxygen saturation in arterial blood (SaO2) provides information about the performance of the respiratory system. Non-invasive measurement of SaO2 by commercial pulse oximeters (SpO2) make use of photoplethysmographic pulses in the red and infrared regions and utilizes the different spectra of light absorption by oxygenated and de-oxygenated hemoglobin. Because light scattering and optical path-lengths differ between the two wavelengths, commercial pulse oximeters require empirical calibration which is based on SaO2 measurement in extracted arterial blood. They are still prone to error, because the path-lengths difference between the two wavelengths varies among different subjects. We have developed modified pulse oximetry, which makes use of two nearby infrared wavelengths that have relatively similar scattering constants and path-lengths and does not require an invasive calibration step. In measurements performed on adults during breath holding, the two-infrared pulse oximeter and a commercial pulse oximeter showed similar changes in SpO2. The two pulse oximeters showed similar accuracy when compared to SaO2 measurement in extracted arterial blood (the gold standard) performed in intensive care units on newborns and children with an arterial line. Errors in SpO2 because of variability in path-lengths difference between the two wavelengths are expected to be smaller in the two-infrared pulse oximeter. Full article
(This article belongs to the Special Issue Sensors for Biosignal Processing)
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Open AccessArticle
Improvement of Left Ventricular Ejection Time Measurement in the Impedance Cardiography Combined with the Reflection Photoplethysmography
Sensors 2018, 18(9), 3036; https://doi.org/10.3390/s18093036 - 11 Sep 2018
Cited by 2
Abstract
Cardiac stroke volume (SV) is an essential hemodynamic indicator that can be used to assess whether the pump function of the heart is normal. Non-invasive SV measurement is currently performed using the impedance cardiography (ICG). In this technology, left ventricular ejection time (LVET) [...] Read more.
Cardiac stroke volume (SV) is an essential hemodynamic indicator that can be used to assess whether the pump function of the heart is normal. Non-invasive SV measurement is currently performed using the impedance cardiography (ICG). In this technology, left ventricular ejection time (LVET) is an important parameter which can be determined from the ICG signals. However, the ICG signals are inherently susceptible to artificial noise interference, which leads to an inaccurate LVET measurement and then yields an error in the calculation of SV. Therefore, the goal of the study was to measure LVETs using both the transmission and reflection photoplethysmography (PPG), and to assess whether the measured LVET was more accurate by the PPG signal than the ICG signal. The LVET measured by the phonocardiography (PCG) was used as the standard for comparing with those by the ICG and PPG. The study recruited ten subjects whose LVETs were simultaneously measured by the ICG using four electrodes, the reflection PPG using neck sensors (PPGneck) and the transmission PPG using finger sensors (PPGfinger). In each subject, ten LVETs were obtained from ten heartbeats selected properly from one-minute recording. The differences of the measured LVETs between the PCG and one of the ICG, PPGneck and PPGfinger were −68.2 ± 148.6 ms, 4.8 ± 86.5 ms and −7.0 ± 107.5 ms, respectively. As compared with the PCG, both the ICG and PPGfinger underestimated but the PPGneck overestimated the LVETs. Furthermore, the measured LVET by the PPGneck was the closest to that by the PCG. Therefore, the PPGneck may be employed to improve the LVET measurement in applying the ICG for continuous monitoring of SV in clinical settings. Full article
(This article belongs to the Special Issue Sensors for Biosignal Processing)
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Open AccessFeature PaperArticle
Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear Features of Electrocardiogram Signals
Sensors 2018, 18(7), 2090; https://doi.org/10.3390/s18072090 - 29 Jun 2018
Cited by 2
Abstract
We developed an automated approach to differentiate between different types of arrhythmic episodes in electrocardiogram (ECG) signals, because, in real-life scenarios, a software application does not know in advance the type of arrhythmia a patient experiences. Our approach has four main stages: (1) [...] Read more.
We developed an automated approach to differentiate between different types of arrhythmic episodes in electrocardiogram (ECG) signals, because, in real-life scenarios, a software application does not know in advance the type of arrhythmia a patient experiences. Our approach has four main stages: (1) Classification of ventricular fibrillation (VF) versus non-VF segments—including atrial fibrillation (AF), ventricular tachycardia (VT), normal sinus rhythm (NSR), and sinus arrhythmias, such as bigeminy, trigeminy, quadrigeminy, couplet, triplet—using four image-based phase plot features, one frequency domain feature, and the Shannon entropy index. (2) Classification of AF versus non-AF segments. (3) Premature ventricular contraction (PVC) detection on every non-AF segment, using a time domain feature, a frequency domain feature, and two features that characterize the nonlinearity of the data. (4) Determination of the PVC patterns, if present, to categorize distinct types of sinus arrhythmias and NSR. We used the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, Creighton University’s VT arrhythmia database, the MIT-BIH atrial fibrillation database, and the MIT-BIH malignant ventricular arrhythmia database to test our algorithm. Binary decision tree (BDT) and support vector machine (SVM) classifiers were used in both stage 1 and stage 3. We also compared our proposed algorithm’s performance to other published algorithms. Our VF detection algorithm was accurate, as in balanced datasets (and unbalanced, in parentheses) it provided an accuracy of 95.1% (97.1%), sensitivity of 94.5% (91.1%), and specificity of 94.2% (98.2%). The AF detection was accurate, as the sensitivity and specificity in balanced datasets (and unbalanced, in parentheses) were found to be 97.8% (98.6%) and 97.21% (97.1%), respectively. Our PVC detection algorithm was also robust, as the accuracy, sensitivity, and specificity were found to be 99% (98.1%), 98.0% (96.2%), and 98.4% (99.4%), respectively, for balanced and (unbalanced) datasets. Full article
(This article belongs to the Special Issue Sensors for Biosignal Processing)
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Open AccessArticle
Adaptive Noise Reduction Algorithm to Improve R Peak Detection in ECG Measured by Capacitive ECG Sensors
Sensors 2018, 18(7), 2086; https://doi.org/10.3390/s18072086 - 29 Jun 2018
Cited by 1
Abstract
Electrocardiograms (ECGs) can be conveniently obtained using capacitive ECG sensors. However, motion noise in measured ECGs can degrade R peak detection. To reduce noise, properties of reference signal and ECG measured by the sensors are analyzed and a new method of active noise [...] Read more.
Electrocardiograms (ECGs) can be conveniently obtained using capacitive ECG sensors. However, motion noise in measured ECGs can degrade R peak detection. To reduce noise, properties of reference signal and ECG measured by the sensors are analyzed and a new method of active noise cancellation (ANC) is proposed in this study. In the proposed algorithm, the original ECG signal at QRS interval is regarded as impulsive noise because the adaptive filter updates its weight as if impulsive noise is added. As the proposed algorithm does not affect impulsive noise, the original signal is not reduced during ANC. Therefore, the proposed algorithm can conserve the power of the original signal within the QRS interval and reduce only the power of noise at other intervals. The proposed algorithm was verified through comparisons with recent research using data from both indoor and outdoor experiments. The proposed algorithm will benefit a noise reduction of noisy biomedical signal measured from sensors. Full article
(This article belongs to the Special Issue Sensors for Biosignal Processing)
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Open AccessArticle
Study of the Home-Auxiliary Robot Based on BCI
Sensors 2018, 18(6), 1779; https://doi.org/10.3390/s18061779 - 01 Jun 2018
Cited by 4
Abstract
A home-auxiliary robot platform is developed in the current study which could assist patients with physical disabilities and older persons with mobility impairments. The robot, mainly controlled by brain computer interface (BCI) technology, can not only perform actions in a person’s field of [...] Read more.
A home-auxiliary robot platform is developed in the current study which could assist patients with physical disabilities and older persons with mobility impairments. The robot, mainly controlled by brain computer interface (BCI) technology, can not only perform actions in a person’s field of vision, but also work outside the field of vision. The wavelet decomposition (WD) is used in this study to extract the δ (0~4 Hz) and θ (4~8 Hz) sub-bands of subjects’ electroencephalogram (EEG) signals. The correlation between pairs of 14 EEG channels is determined with synchronization likelihood (SL), and the brain network structure is generated. Then, the motion characteristics are analyzed using the brain network parameters clustering coefficient (C) and global efficiency (G). Meanwhile, the eye movement characteristics in the F3 and F4 channels are identified. Finally, the motion characteristics identified by brain networks and eye movement characteristics can be used to control the home-auxiliary robot platform. The experimental result shows that the accuracy rate of left and right motion recognition using this method is more than 93%. Additionally, the similarity between that autonomous return path and the real path of the home-auxiliary robot reaches up to 0.89. Full article
(This article belongs to the Special Issue Sensors for Biosignal Processing)
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Open AccessArticle
Analysis of Consistency of Transthoracic Bioimpedance Measurements Acquired with Dry Carbon Black PDMS Electrodes, Adhesive Electrodes, and Wet Textile Electrodes
Sensors 2018, 18(6), 1719; https://doi.org/10.3390/s18061719 - 26 May 2018
Cited by 2
Abstract
The detection of intrathoracic volume retention could be crucial to the early detection of decompensated heart failure (HF). Transthoracic Bioimpedance (TBI) measurement is an indirect, promising approach to assessing intrathoracic fluid volume. Gel-based adhesive electrodes can produce skin irritation, as the patient needs [...] Read more.
The detection of intrathoracic volume retention could be crucial to the early detection of decompensated heart failure (HF). Transthoracic Bioimpedance (TBI) measurement is an indirect, promising approach to assessing intrathoracic fluid volume. Gel-based adhesive electrodes can produce skin irritation, as the patient needs to place them daily in the same spots. Textile electrodes can reduce skin irritation; however, they inconveniently require wetting before each use and provide poor adherence to the skin. Previously, we developed waterproof reusable dry carbon black polydimethylsiloxane (CB/PDMS) electrodes that exhibited a good response to motion artifacts. We examined whether these CB/PDMS electrodes were suitable sensing components to be embedded into a monitoring vest for measuring TBI and the electrocardiogram (ECG). We recruited N = 20 subjects to collect TBI and ECG data. The TBI parameters were different between the various types of electrodes. Inter-subject variability for copper-mesh CB/PDMS electrodes and Ag/AgCl electrodes was lower compared to textile electrodes, and the intra-subject variability was similar between the copper-mesh CB/PDMS and Ag/AgCl. We concluded that the copper mesh CB/PDMS (CM/CB/PDMS) electrodes are a suitable alternative for textile electrodes for TBI measurements, but with the benefit of better skin adherence and without the requirement of wetting the electrodes, which can often be forgotten by the stressed HF subjects. Full article
(This article belongs to the Special Issue Sensors for Biosignal Processing)
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Review

Jump to: Research

Open AccessReview
Recent Research and Developing Trends of Wearable Sensors for Detecting Blood Pressure
Sensors 2018, 18(9), 2772; https://doi.org/10.3390/s18092772 - 23 Aug 2018
Cited by 6
Abstract
Blood pressure is considered an index to measure a person’s health or state. The IEEE published a standard for wearable cuffless blood pressure measuring devices, which was certified as IEEE1708 on 26 August 2014, and, according to this standard, the development of wearable [...] Read more.
Blood pressure is considered an index to measure a person’s health or state. The IEEE published a standard for wearable cuffless blood pressure measuring devices, which was certified as IEEE1708 on 26 August 2014, and, according to this standard, the development of wearable devices based on blood pressure is expected in the future. Considering this, blood pressure should be detectable all the time and everywhere, and this can help improve health consciousness. In this review, we introduce the recent development of wearable blood pressure measuring devices and research trends, and present the future prospects for blood pressure measuring devices. Full article
(This article belongs to the Special Issue Sensors for Biosignal Processing)
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