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Wearable Biomedical Sensors for Mobile Health
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Wearable Biomedical Sensors for Mobile Health

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

Deadline for manuscript submissions: 20 May 2026 | Viewed by 12539

Special Issue Editors

Prof. Dr. Hesham H. Ali

E-Mail Website
Guest Editor
UNO Bioinformatics Core Facility, College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
Interests: bioinformatics; graph theory; design and analysis of algorithms; graph modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ka-Chun (Joseph) Siu

E-Mail Website
Guest Editor
Department of Health and Rehabilitations Sciences, University of Nebraska Medical Center, Omaha, NE 68198-4420, USA
Interests: human movement; gait biomechanics; motor learning; rehabilitation; biomechanics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The last several years have witnessed major advancements in the development of sensor technologies and wearable devices with the goal of collecting various types of data in many application domains. Based on such technologies, many commercial products have swamped the market and found their way on the wrists, ancles, and belts of many users. Although these developments are certainly welcomed, there is so much left to be accomplished in order to take full advantage of the data gathered by such devices. The most critical missing component is the lack of advanced data analytics. In the case of health monitoring, like many aspects of healthcare, the focus has been primarily on producing devices with data collection capabilities rather than developing advanced models for analyzing the available data.

In this Special Issue of Sensors, we attempt to fill this gap by soliciting papers related to data analytics that connect mobility and heath. We invite papers related to algorithms, tools, and approaches that can be applied to analyze big mobility data and reveal new useful health-related features. We also welcome papers related to how mobility data can be integrated with biological and medical data to obtain accurate signals that can be used to diagnose various health conditions and assess the effectiveness of different medical treatments. We hope the selected articles will pave the way towards a new decision support system that leads to new discoveries in biomedical research and healthcare applications.

Prof. Dr. Hesham H. Ali
Prof. Dr. Ka-Chun (Joseph) Siu
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 250 words) can be sent to the Editorial Office for assessment.

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

  • wearable devices
  • mobility data
  • mobile health biomedical informatics
  • mobility for health

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Published Papers (3 papers)

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Research

21 pages, 297 KB  
Article
Resting Heart Rate Variability Measured by Consumer Wearables and Its Associations with Diverse Health Domains in Five Longitudinal Studies
by Raymond Hernandez, Stefan Schneider, Herman J. de Vries, Jason Fanning, Dominic Ehrmann, Haomiao Jin, Raeanne C. Moore, Shannon Juengst, Aaron Striegel, Jack P. Ginsberg, Norbert Hermanns and Arthur A. Stone
Sensors 2025, 25(23), 7147; https://doi.org/10.3390/s25237147 - 22 Nov 2025
Viewed by 6390
Abstract
Heart rate variability (HRV) is widely recognized as an indicator of general health, particularly time domain measures like the root mean square of successive differences (RMSSD) between consecutive heartbeats. Consumer wearables measuring HRV have potential for wide accessibility meaning that their broad use [...] Read more.
Heart rate variability (HRV) is widely recognized as an indicator of general health, particularly time domain measures like the root mean square of successive differences (RMSSD) between consecutive heartbeats. Consumer wearables measuring HRV have potential for wide accessibility meaning that their broad use to capture HRV as a health biomarker is possible. Our objective was to investigate the validity of HRV measured by wearables as a general health indicator. We examined whether resting HRV assessed by wearables across five studies—two using smartwatches, two using heart rate chest straps, and one using a smartring—exhibited expected associations with diverse health domains, including mental, physical, behavioral, functional, and physiological. We focused on resting HRV measures recorded while in primarily stationary conditions, either upon waking or while sleeping, because such measures would theoretically reduce the effects of potential confounders such as movement artifacts, daytime caffeine intake, and postural changes. Wearables measured resting HRV had small-to-moderate associations with more clinically oriented and trait-like (or slow-changing) health measures like Hba1c (average blood glucose, r = −0.21, p = 0.014), depressive symptoms (r = −0.22, p = 0.024), and sleep difficulty (r = −0.11, p = 0.003). Wearable-measured resting HRV can potentially serve as a health biomarker, but further research is needed. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors for Mobile Health)
16 pages, 1107 KB  
Article
Validation of the Polar Fitness Test for Estimation of Maximal Oxygen Consumption at Rest in Medically Supervised Exercise Training: Comparison with CPET and the 6-Minute Walk Test
by Michael Neudorfer, Lukas Ötzlinger, Devender Kumar, Josef Niebauer, Jan David Smeddinck, Mahdi Sareban and Gunnar Treff
Sensors 2025, 25(18), 5649; https://doi.org/10.3390/s25185649 - 10 Sep 2025
Viewed by 2003
Abstract
The Polar Fitness Test (PFT) estimates maximal oxygen consumption (V̇O2max) under resting conditions using heart rate data from the manufacturer’s wearable devices. We aimed to validate the PFT in a population with cardiovascular risk factors and to compare [...] Read more.
The Polar Fitness Test (PFT) estimates maximal oxygen consumption (V̇O2max) under resting conditions using heart rate data from the manufacturer’s wearable devices. We aimed to validate the PFT in a population with cardiovascular risk factors and to compare its results with five established equations predicting V̇O2max based on the 6-min walk test (6MWT). Twenty-four participants (9 female; age 57.4 ± 10.2 years) undergoing medically supervised exercise training—including seven individuals on heart rate-limiting medication—completed the PFT, 6MWT, and cardiopulmonary exercise testing (CPET), which served as the criterion V̇O2max measurement. The PFT showed a mean absolute percent-age error (MAPE) of 13.7%, an intraclass correlation coefficient (ICC) of 0.743, a mean bias of −1.0 mL/min/kg, and limits of agreement (LoA) of ±11.4 mL/min/kg compared to CPET. Among the 6MWT-based equations, only the Porcari equation demonstrated similar performance (MAPE 12.6%, ICC 0.725, mean bias 0.2 mL/min/kg, LoA ± 9.7 mL/min/kg), while other equations showed larger errors and systematic deviations. Our data indicate that the PFT may present an easily accessible option to estimate V̇O2max on population level when exercise-based testing is not feasible. However, its variability limits use for individual clinical decisions, reaffirming the relevance of CPET for accurate assessment. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors for Mobile Health)
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15 pages, 3954 KB  
Article
A Wireless Smart Adhesive Integrated with a Thin-Film Stretchable Inverted-F Antenna
by Ashok Chhetry, Hodam Kim and Yun Soung Kim
Sensors 2024, 24(22), 7155; https://doi.org/10.3390/s24227155 - 7 Nov 2024
Viewed by 2867
Abstract
In recent years, skin-mounted devices have gained prominence in personal wellness and remote patient care. However, the rigid components of many wearables often cause discomfort due to their mechanical mismatch with the skin. To address this, we extend the use of the solderable [...] Read more.
In recent years, skin-mounted devices have gained prominence in personal wellness and remote patient care. However, the rigid components of many wearables often cause discomfort due to their mechanical mismatch with the skin. To address this, we extend the use of the solderable stretchable sensing system (S4) to develop a wireless skin temperature-sensing smart adhesive. This work introduces two novel types of progress in wearables: the first demonstration of Bluetooth-integration and development of a thin-film-based stretchable inverted-F antenna (SIFA). Characterized through RF simulations, vector network analysis under deformation, and anechoic chamber tests, SIFA demonstrated potential as a low-profile, on-body Bluetooth antenna with a resonant frequency of 2.45 GHz that helps S4 retain its thin overall profile. The final S4 system achieved high correlation (R = 0.95, p < 0.001, mean standard error = 0.04 °C) with commercial sensors during daily activities. These findings suggest that S4-based smart adhesives integrated with SIFAs could offer a promising platform for comfortable, efficient, and functional skin-integrated wearables, supporting a range of health monitoring applications. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors for Mobile Health)
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