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Smart Device and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 27411

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Special Issue Editors

Prof. Dr. Christian Veltmann

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Guest Editor

Elektrophysiologie Bremen, Bremen, Germany
Interests: sudden cardiac death; cardioverter–defibrillator; heart failure; primary electrical diseases
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. David Duncker

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Guest Editor

Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, 30625 Hannover, Germany
Interests: arrhythmias pacemakers; atrial fibrillation; heart failure; cardiac electrophysiology; cardiology chronic; electrocardiography; cardiomyopathies; cardiac function
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Heart rate monitoring has huge potential in disease prevention, stroke prediction, and mental stress/workload assessment. In the meantime, wearable sensors including smartphones or smart devices have allowed for non-invasive and continuous measurement of heart rate variability. We invite investigators to contribute with original research articles as well as review articles that will stimulate continuing efforts to more effectively apply wearable sensors or smart devices in monitoring heart rate variability in everyday life or for medical purposes.

Prof. Dr. Christian Veltmann
Prof. Dr. David Duncker
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

heart rate monitoring
heart rate variability (HRV)
heart failure
cardiology chronic
cardiac function
cardiac electrocardiography (ECG)
smartphone
wearables
cardiomyopathies

Published Papers (6 papers)

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Research

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16 pages, 7774 KiB

Open AccessArticle

Signal Quality Analysis of Single-Arm Electrocardiography

by Jia-Jung Wang, Shing-Hong Liu, Cheng-Hsien Tsai, Ioannis Manousakas, Xin Zhu and Thung-Lip Lee

Sensors 2023, 23(13), 5818; https://doi.org/10.3390/s23135818 - 22 Jun 2023

Cited by 1 | Viewed by 1104

Abstract

The number of people experiencing mental stress or emotional dysfunction has increased since the onset of the COVID-19 pandemic, as many individuals have had to adapt their daily lives. Numerous studies have demonstrated that mental health disorders can pose a risk for certain diseases, and they are also closely associated with the problem of mental workload. Now, wearable devices and mobile health applications are being utilized to monitor and assess individuals’ mental health conditions on a daily basis using heart rate variability (HRV), typically measured by the R-to-R wave interval (RRI) of an electrocardiogram (ECG). However, portable or wearable ECG devices generally require two electrodes to perform bipolar limb leads, such as the Einthoven triangle. This study aims to develop a single-arm ECG measurement method, with lead I ECG serving as the gold standard. We conducted static and dynamic experiments to analyze the morphological performance and signal-to-noise ratio (SNR) of the single-arm ECG. Three morphological features were defined, RRI, the duration of the QRS complex wave, and the amplitude of the R wave. Thirty subjects participated in this study. The results indicated that RRI exhibited the highest cross-correlation (R = 0.9942) between the single-arm ECG and lead I ECG, while the duration of the QRS complex wave showed the weakest cross-correlation (R = 0.2201). The best SNR obtained was 26.1 ± 5.9 dB during the resting experiment, whereas the worst SNR was 12.5 ± 5.1 dB during the raising and lowering of the arm along the z-axis. This single-arm ECG measurement method offers easier operation compared to traditional ECG measurement techniques, making it applicable for HRV measurement and the detection of an irregular RRI. Full article

(This article belongs to the Special Issue Smart Device and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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21 pages, 1092 KiB

Open AccessArticle

Heart Rate Variability Control Using a Biofeedback and Wearable System

by Eduardo Viera, Hector Kaschel and Claudio Valencia

Sensors 2022, 22(19), 7153; https://doi.org/10.3390/s22197153 - 21 Sep 2022

Cited by 2 | Viewed by 2574

Abstract

Heart rate variability is an important physiological parameter in medicine. This parameter is used as an indicator of physiological and psychological well-being and even of certain pathologies. Research on biofeedback integrates the fields of biological application (physiological behavior), system modeling, and automated control. This study proposes a new method for modeling and controlling heart rate variability as heart rate acceleration, a model expressed in the frequency domain. The model is obtained from excitation and response signals from heart rate variability, which through the instrumental variables method and the minimization of a cost function delivers a transfer function that represents the physiological phenomenon. This study also proposes the design of an adaptive controller using the reference model. The controller controls heart rate variability based on the light actuators designed here, generating a conditioned reflex that allows individuals to self-regulate their state through biofeedback, synchronizing this action to homeostasis. Modeling is conducted in a target population of middle-aged men who work as firefighters and forest firefighters. This study validates the proposed model, as well as the design of the controllers and actuators, through a simple experiment based on indoor cycling. This experiment has different segments, namely leaving inertia, non-controlled segment, and actively controlled segment. Full article

(This article belongs to the Special Issue Smart Device and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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Graphical abstract

12 pages, 3127 KiB

Open AccessCommunication

Novel Design of a Multimodal Technology-Based Smart Stethoscope for Personal Cardiovascular Health Monitoring

by Heejoon Park, Qun Wei, Soomin Lee and Miran Lee

Sensors 2022, 22(17), 6465; https://doi.org/10.3390/s22176465 - 27 Aug 2022

Cited by 7 | Viewed by 2797

Abstract

Heart sounds and heart rate (pulse) are the most common physiological signals used in the diagnosis of cardiovascular diseases. Measuring these signals using a device and analyzing their interrelationships simultaneously can improve the accuracy of existing methods and propose new approaches for the diagnosis of cardiovascular diseases. In this study, we have presented a novel smart stethoscope based on multimodal physiological signal measurement technology for personal cardiovascular health monitoring. The proposed device is designed in the shape of a compact personal computer mouse for easy grasping and attachment to the surface of the chest using only one hand. A digital microphone and photoplehysmogram sensor are installed on the bottom and top surfaces of the device, respectively, to measure heart sound and pulse from the user’s chest and finger simultaneously. In addition, a high-performance Bluetooth Low Energy System-on-Chip ARM microprocessor is used for pre-processing of measured data and communication with the smartphone. The prototype is assembled on a manufactured printed circuit board and 3D-printed shell to conduct an in vivo experiment to test the performance of physiological signal measurement and usability by observing users’ muscle fatigue variation. Full article

(This article belongs to the Special Issue Smart Device and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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19 pages, 3610 KiB

Open AccessArticle

Quantitative Analysis Using Consecutive Time Window for Unobtrusive Atrial Fibrillation Detection Based on Ballistocardiogram Signal

by Tianqing Cheng, Fangfang Jiang, Qing Li, Jitao Zeng and Biyong Zhang

Sensors 2022, 22(15), 5516; https://doi.org/10.3390/s22155516 - 24 Jul 2022

Cited by 1 | Viewed by 1719

Abstract

Atrial fibrillation (AF) is the most common clinically significant arrhythmia; therefore, AF detection is crucial. Here, we propose a novel feature extraction method to improve AF detection performance using a ballistocardiogram (BCG), which is a weak vibration signal on the body surface transmitted by the cardiogenic force. In this paper, continuous time windows (CTWs) are added to each BCG segment and recurrence quantification analysis (RQA) features are extracted from each time window. Then, the number of CTWs is discussed and the combined features from multiple time windows are ranked, which finally constitute the CTW–RQA features. As validation, the CTW–RQA features are extracted from 4000 BCG segments of 59 subjects, which are compared with classical time and time-frequency features and up-to-date energy features. The accuracy of the proposed feature is superior, and three types of features are fused to obtain the highest accuracy of 95.63%. To evaluate the importance of the proposed feature, the fusion features are ranked using a chi-square test. CTW–RQA features account for 60% of the first 10 fusion features and 65% of the first 17 fusion features. It follows that the proposed CTW–RQA features effectively supplement the existing BCG features for AF detection. Full article

(This article belongs to the Special Issue Smart Device and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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Review

Jump to: Research

14 pages, 274 KiB

Open AccessReview

The Future of Stress Management: Integration of Smartwatches and HRV Technology

by Ravinder Jerath, Mohammad Syam and Shajia Ahmed

Sensors 2023, 23(17), 7314; https://doi.org/10.3390/s23177314 - 22 Aug 2023

Cited by 3 | Viewed by 9855

Abstract

In the modern world, stress has become a pervasive concern that affects individuals’ physical and mental well-being. To address this issue, many wearable devices have emerged as potential tools for stress detection and management by measuring heart rate, heart rate variability (HRV), and various metrics related to it. This literature review aims to provide a comprehensive analysis of existing research on HRV tracking and biofeedback using smartwatches pairing with reliable 3rd party mobile apps like Elite HRV, Welltory, and HRV4Training specifically designed for stress detection and management. We apply various algorithms and methodologies employed for HRV analysis and stress detection including time-domain, frequency-domain, and non-linear analysis techniques. Prominent smartwatches, such as Apple Watch, Garmin, Fitbit, Polar, and Samsung Galaxy Watch, are evaluated based on their HRV measurement accuracy, data quality, sensor technology, and integration with stress management features. We describe the efficacy of smartwatches in providing real-time stress feedback, personalized stress management interventions, and promoting overall well-being. To assist researchers, doctors, and developers with using smartwatch technology to address stress and promote holistic well-being, we discuss the data’s advantages and limitations, future developments, and the significance of user-centered design and personalized interventions. Full article

(This article belongs to the Special Issue Smart Device and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

22 pages, 4119 KiB

Open AccessReview

Wearable Devices for Remote Monitoring of Heart Rate and Heart Rate Variability—What We Know and What Is Coming

by Navya Alugubelli, Hussam Abuissa and Attila Roka

Sensors 2022, 22(22), 8903; https://doi.org/10.3390/s22228903 - 17 Nov 2022

Cited by 19 | Viewed by 8464

Abstract

Heart rate at rest and exercise may predict cardiovascular risk. Heart rate variability is a measure of variation in time between each heartbeat, representing the balance between the parasympathetic and sympathetic nervous system and may predict adverse cardiovascular events. With advances in technology and increasing commercial interest, the scope of remote monitoring health systems has expanded. In this review, we discuss the concepts behind cardiac signal generation and recording, wearable devices, pros and cons focusing on accuracy, ease of application of commercial and medical grade diagnostic devices, which showed promising results in terms of reliability and value. Incorporation of artificial intelligence and cloud based remote monitoring have been evolving to facilitate timely data processing, improve patient convenience and ensure data security. Full article

(This article belongs to the Special Issue Smart Device and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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