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Sensor Technologies for Human Health Monitoring: 2nd Edition
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Sensor Technologies for Human Health Monitoring: 2nd Edition

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

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 5865

Special Issue Editor

Dr. Helmut Karl Lackner

E-Mail Website
Guest Editor
Department of Physiology, Medical University of Graz, 8036 Graz, Austria
Interests: control mechanisms of heart rate dynamics; heart rate variability; short-term blood pressure regulation; signal preprocessing techniques; psychophysiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Both scientific and medical staff, as well as non-professionals, benefit tremendously from recent advances in technology, such as wearable biomedical sensors in smart clothing and smart mobile devices; these have enabled human health monitoring of a high technical quality. For example, measuring heart rate variability via smart mobile devices provides a seemingly simple opportunity to examine the interaction between sympathetic and parasympathetic nervous system activities in a non-invasive manner, which may deliver useful information regarding a variety of physiological situations. Unsurprisingly, these developments have also caught the interest of professionals in non-medical fields. However, even experienced users and researchers may not always be fully aware of all the fundamental principles and weaknesses of the measures they employ, and thus may not be immune to occasionally stumbling into an interpretational pitfall.

Therefore, this Special Issue aims to address recent advances in hardware and software developments with the aim of providing some support for their validity, and presents detailed methodological clarification and new data material for illustration.

Dr. Helmut Karl Lackner
Guest Editor

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

  • human health monitoring
  • physiological measurements
  • wearable biomedical sensing
  • methodological considerations
  • signal preprocessing
  • artifact detection
  • validity and reliability check

Related Special Issue

Published Papers (5 papers)

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Research

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11 pages, 1514 KiB  
Article
Screening for Peripheral Vascular Stiffness in Lipedema Patients by Automatic Electrocardiogram-Based Oscillometric Detection
by Adrian Mahlmann, Yazan Khorzom, Christian-Alexander Behrendt, Jennifer Lynne Leip, Martin Bachler, Siegfried Wassertheurer, Nesma Elzanaty and Tamer Ghazy
Sensors 2024, 24(5), 1673; https://0-doi-org.brum.beds.ac.uk/10.3390/s24051673 - 5 Mar 2024
Viewed by 699
Abstract
Body mass index (BMI) is seen as a predictor of cardiovascular disease (CVD) in lipedema patients. A valid predictor of CVD is increased aortic stiffness (IAS), and previous research described IAS in lipedema. However, it is not known if this applies to all [...] Read more.
Body mass index (BMI) is seen as a predictor of cardiovascular disease (CVD) in lipedema patients. A valid predictor of CVD is increased aortic stiffness (IAS), and previous research described IAS in lipedema. However, it is not known if this applies to all patients. In this cross-sectional single-center cohort study, peripheral pulse wave velocity (PWV) as a non-invasive indicator of aortic stiffness was measured in 41 patients with lipedema, irrespective of stage and without pre-existing cardiovascular conditions or a history of smoking and a maximum body mass index (BMI) of 35 kg/m2. Automatically electrocardiogram-triggered oscillometric sensor technology by the Gesenius–Keller method was used. Regardless of the stage of lipedema disease, there was no significant difference in PWV compared to published standard values adjusted to age and blood pressure. BMI alone is not a predictor of cardiovascular risk in lipedema patients. Measuring other anthropometric factors, such as the waist–hip ratio or waist–height ratio, should be included, and the existing cardiovascular risk factors, comorbidities, and adipose tissue distribution for accurate risk stratification should be taken into account. Automated sensor technology recording the PWV represents a valid and reliable method for health monitoring and early detection of cardiovascular risks. Full article
(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring: 2nd Edition)
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10 pages, 675 KiB  
Article
Effect of Prenatal Yoga versus Moderate-Intensity Walking on Cardiorespiratory Adaptation to Acute Psychological Stress: Insights from Non-Invasive Beat-to-Beat Monitoring
by Miha Lučovnik, Helmut K. Lackner, Ivan Žebeljan, Manfred G. Moertl, Izidora Vesenjak Dinevski, Adrian Mahlmann and Dejan Dinevski
Sensors 2024, 24(5), 1596; https://0-doi-org.brum.beds.ac.uk/10.3390/s24051596 - 29 Feb 2024
Viewed by 822
Abstract
We recently reported enhanced parasympathetic activation at rest throughout pregnancy associated with regular yoga practice. The present study presents a secondary analysis of data collected within a prospective cohort study of 33 pregnant women practicing yoga once weekly throughout pregnancy and 36 controls [...] Read more.
We recently reported enhanced parasympathetic activation at rest throughout pregnancy associated with regular yoga practice. The present study presents a secondary analysis of data collected within a prospective cohort study of 33 pregnant women practicing yoga once weekly throughout pregnancy and 36 controls not involved in formal pregnancy exercise programs. The objective was to assess the impact of prenatal yoga on the autonomic nervous system stress response. Healthy pregnant women with singleton pregnancies were recruited in the first trimester. There was no significant difference in the maternal body mass index (BMI) between the yoga group and the controls (24.06 ± 3.55 vs. 23.74 ± 3.43 kg/m2, p = 0.693). Women practicing yoga were older (28.6 ± 3.9 vs. 31.3 ± 3.5 years, p = 0.005) and more often nulliparous (26 (79%) vs. 18 (50%), p = 0.001). We studied heart rate variability (HRV) parameters in the time domain (SDNN, standard deviation of regular R-R intervals, and RMSSD, square root of mean squared differences of successive R-R intervals) and frequency domain (ln(LF/HF), natural logarithm of low-frequency to high-frequency power), as well as synchronization indices of heart rate, blood pressure and respiration during and immediately following acute psychological stress of a standardized mental challenge test. Measurements were performed once per trimester before and after yoga or a 30 min moderate-intensity walk. Statistical comparison was performed using three-way analyses of variance (p < 0.05 significant). Time domain HRV parameters during and following mental challenge in the yoga group were significantly higher compared to the controls regardless of the trimester (F = 7.22, p = 0.009 for SDNN and F = 9.57, p = 0.003 for RMSSD, respectively). We observed no significant differences in the yoga group vs. the controls in terms of ln(LF/HF) and synchronization indices. Regular prenatal yoga practice was associated with a significantly reduced sympathetic response to mental challenge and quicker recovery after acute psychological stress. These effects persisted throughout pregnancy with regular practice. Full article
(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring: 2nd Edition)
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18 pages, 1143 KiB  
Article
Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques
by Laura Fontes, Pedro Machado, Doratha Vinkemeier, Salisu Yahaya, Jordan J. Bird and Isibor Kennedy Ihianle
Sensors 2024, 24(4), 1096; https://0-doi-org.brum.beds.ac.uk/10.3390/s24041096 - 7 Feb 2024
Cited by 1 | Viewed by 1474
Abstract
Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This paper addresses the critical need for accurate [...] Read more.
Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This paper addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, it proposes an efficient deep learning approach for stress detection from facial videos. In contrast to the research on wearable devices, this paper proposes novel Hybrid Deep Learning (DL) networks for stress detection based on remote photoplethysmography (rPPG), employing (Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), 1D Convolutional Neural Network (1D-CNN)) models with hyperparameter optimisation and augmentation techniques to enhance performance. The proposed approach yields a substantial improvement in accuracy and efficiency in stress detection, achieving up to 95.83% accuracy with the UBFC-Phys dataset while maintaining excellent computational efficiency. The experimental results demonstrate the effectiveness of the proposed Hybrid DL models for rPPG-based-stress detection. Full article
(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring: 2nd Edition)
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12 pages, 908 KiB  
Article
Use of a Single-Item Ecological Momentary Assessment to Measure Daily Exercise: Agreement with Accelerometer-Measured Exercise
by Kevin Sundquist, Joseph E. Schwartz, Matthew M. Burg, Karina W. Davidson and Keith M. Diaz
Sensors 2024, 24(3), 946; https://0-doi-org.brum.beds.ac.uk/10.3390/s24030946 - 1 Feb 2024
Viewed by 704
Abstract
Accelerometers have been used to objectively quantify physical activity, but they can pose a high burden. This study was conducted to determine the feasibility of using a single-item smartphone-based ecological momentary assessment (EMA) in lieu of accelerometers in long-term assessment of daily exercise. [...] Read more.
Accelerometers have been used to objectively quantify physical activity, but they can pose a high burden. This study was conducted to determine the feasibility of using a single-item smartphone-based ecological momentary assessment (EMA) in lieu of accelerometers in long-term assessment of daily exercise. Data were collected from a randomized controlled trial of intermittently exercising, otherwise healthy adults (N = 79; 57% female, mean age: 31.9 ± 9.5 years) over 365 days. Smartphone-based EMA self-reports of exercise entailed daily end-of-day responses about physical activity; the participants also wore a Fitbit device to measure physical activity. The Kappa statistic was used to quantify the agreement between accelerometer-determined (24 min of moderate-to-vigorous physical activity [MVPA] within 30 min) and self-reported exercise. Possible demographic predictors of agreement were assessed. Participants provided an average of 164 ± 87 days of complete data. The average within-person Kappa was κ = 0.30 ± 0.22 (range: −0.15–0.73). Mean Kappa ranged from 0.16 to 0.30 when the accelerometer-based definition of an exercise bout varied in duration from 15 to 30 min of MVPA within any 30 min period. Among the correlates examined, sex was significantly associated with agreement; mean agreement was higher among women (κ = 0.37) than men (κ = 0.20). Agreement between EMA self-reported and accelerometer-measured exercise was fair, suggesting that long-term exercise monitoring through a single-item EMA may be acceptable. Full article
(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring: 2nd Edition)
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Review

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33 pages, 3914 KiB  
Review
A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias
by Seyedeh Somayyeh Mousavi, Matthew A. Reyna, Gari D. Clifford and Reza Sameni
Sensors 2024, 24(6), 1730; https://0-doi-org.brum.beds.ac.uk/10.3390/s24061730 - 7 Mar 2024
Cited by 2 | Viewed by 1689
Abstract
Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices has been predominantly driven by the increased prevalence of hypertension [...] Read more.
Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices has been predominantly driven by the increased prevalence of hypertension and its associated risks and clinical conditions. Recent guidelines advocate for regular BP monitoring as part of regular clinical visits or even at home. This increased utilization of BP measurement technologies has raised significant concerns regarding the accuracy of reported BP values across settings. In this survey, which focuses mainly on cuff-based BP monitoring technologies, we highlight how BP measurements can demonstrate substantial biases and variances due to factors such as measurement and device errors, demographics, and body habitus. With these inherent biases, the development of a new generation of cuff-based BP devices that use artificial intelligence (AI) has significant potential. We present future avenues where AI-assisted technologies can leverage the extensive clinical literature on BP-related studies together with the large collections of BP records available in electronic health records. These resources can be combined with machine learning approaches, including deep learning and Bayesian inference, to remove BP measurement biases and provide individualized BP-related cardiovascular risk indexes. Full article
(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring: 2nd Edition)
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