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Wearable Technologies and Sensors for Health Monitoring
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Wearable Technologies and Sensors for Health Monitoring

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

Deadline for manuscript submissions: 20 October 2026 | Viewed by 6237

Editor

Dr. Francisco J. Romero

E-Mail Website
Guest Editor
Department of Electronics and Computer Technology, University of Granada, 18071 Granada, Spain
Interests: wearable; embedded systems; sensors; flexible electronics; printed electronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable health monitoring systems (WHMS) are transforming healthcare by providing non-intrusive, remote-access solutions for continuous biosignal monitoring. These devices enable ubiquitous point-of-care (POC) diagnostics and facilitate rapid medical intervention. Recent advances in this field have been made possible thanks to significant progress in multidisciplinary domains, including biosensors, RF antennas, energy storage devices, processing algorithms, and low-power communication protocols, enabling the development of cost-effective, low-power, and high-performance wearable devices. These innovations contribute to a more accessible and pervasive healthcare system. Consequently, ongoing research in WHMS should continue to explore low-cost materials and novel sensing technologies to further enhance performance, affordability, and integration of these devices.

This Special Issue aims to showcase novel technologies that support advances in WHMS, including advanced sensors, flexible antennas, innovative energy storage solutions, intelligent processing algorithms, and communication networks.

We warmly welcome original research articles, communications, and reviews that address recent developments in wearable technologies and sensors for healthcare applications.

Dr. Francisco J. Romero
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 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-anonymized 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 health monitoring
  • biosignal monitoring
  • biosensors
  • flexible antennas

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Published Papers (4 papers)

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Editorial

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7 pages, 170 KB  
Editorial
Wearable Technology and Sensors for Healthcare and Wellbeing
by Salvatore Tedesco and Dimitrios-Sokratis Komaris
Sensors 2026, 26(8), 2385; https://doi.org/10.3390/s26082385 - 13 Apr 2026
Cited by 2 | Viewed by 1271
Abstract
Wearable technology has undergone explosive growth over recent years, driven by sweeping advances in information and communications technology and shaped by fundamental shifts in demography, lifestyle, and the broader environment [...] Full article
(This article belongs to the Special Issue Wearable Technologies and Sensors for Health Monitoring)

Research

Jump to: Editorial, Other

30 pages, 5988 KB  
Article
BioShield-12: A 3D-Printed Conformal Chest Shield for Wireless 12-Lead ECG Acquisition
by Ahsan Naveed, Rida-e-Fatima, Zia Mohy Ud Din, Abdullah Al Aishan, Hedi Ammar Guesmi and Jahan Zeb Gul
Sensors 2026, 26(15), 4936; https://doi.org/10.3390/s26154936 - 4 Aug 2026
Viewed by 381
Abstract
Reproducible electrode placement remains a critical, unresolved challenge in wearable ECG systems, where manual electrode attachment introduces inter-session positional error that degrades signal morphology and compromises multi-lead representation. Although the 12-lead clinical ECG system is the gold standard, conventional setups are often bulky, [...] Read more.
Reproducible electrode placement remains a critical, unresolved challenge in wearable ECG systems, where manual electrode attachment introduces inter-session positional error that degrades signal morphology and compromises multi-lead representation. Although the 12-lead clinical ECG system is the gold standard, conventional setups are often bulky, wired, and dependent on operator expertise, limiting their use in prehospital or remote care. Recent advancements in wearable and wireless ECG systems have improved mobility and real-time monitoring, but they typically suffer from limited lead coverage, discomfort, and unstable connectivity. This paper introduces BioShield-12, an anatomically adaptive thermoplastic polyurethane (TPU) shield fabricated using fused deposition modeling (FDM) to improve multi-site electrode placement and 12-lead electrocardiogram (ECG) reconstruction from a single-shield design based on anthropometric data from ten healthy adults (five male, five female; sizing cohort). Mechanical characterization confirmed TPU’s suitability as a compliant wearable substrate, with toughness of 34.4 MJ/m3 and elastic modulus of 79.1 MPa. Feasibility validation against a research-grade reference (BIOPAC MP36) in twenty healthy subjects (n = 20; 10 male, 10 female; mean age 21 ± 4 years) demonstrated consistent signal fidelity (SNR: 17.5–22.5 dB; Pearson r: 0.93–0.98; power-line interference ratio (PLIR): 1.0–3.2 × 10−5) and confirmed feasibility of 12-lead reconstruction. This work provides proof-of-concept for an additive manufacturing-based conformal electrode interface that eliminates variability in placement without needing individual attachment. Full article
(This article belongs to the Special Issue Wearable Technologies and Sensors for Health Monitoring)
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25 pages, 4402 KB  
Article
Sleep Stage Classification During CPAP Therapy from CPAP-Airflow and Wearable Fingertip Signals
by Hsin-Yu Chen, Aatif Husain, Andrey V. Zinchuk, Henry K. Yaggi, Muneeb Ahsan, Cheng-Yao Chen, Shirah Pokusa and Hau-Tieng Wu
Sensors 2026, 26(12), 3720; https://doi.org/10.3390/s26123720 - 11 Jun 2026
Viewed by 596
Abstract
Background: Continuous Positive Airway Pressure (CPAP) therapy is the standard treatment for obstructive sleep apnea–hypopnea syndrome (OSAHS), and photoplethysmography (PPG) sensors are commonly used in wearable devices for home sleep apnea testing. The recorded airflow and PPG signals from both sensors capture rich [...] Read more.
Background: Continuous Positive Airway Pressure (CPAP) therapy is the standard treatment for obstructive sleep apnea–hypopnea syndrome (OSAHS), and photoplethysmography (PPG) sensors are commonly used in wearable devices for home sleep apnea testing. The recorded airflow and PPG signals from both sensors capture rich physiological patterns. We hypothesize that by combining information from these signals, we can efficiently estimate sleep dynamics of patients receiving CPAP treatment. Methods: The airflow signals were obtained from CPAP titration devices, denoted as CPAP-airflow, while the PPG signals were collected using the PranaQ TipTraQ (TTQ001), a fingertip-worn wearable device. We separately trained one-dimensional convolutional neural networks for CPAP-airflow and PPG signals and fused their outputs through probabilistic ensembling to predict sleep stages. The ensemble method is a late-fusion soft-voting scheme that computes a linearly weighted combination of synchronized softmax probability vectors from the modality-specific models. Results: For three-stage classification (Wake, REM, NREM), the PPG-based and CPAP-airflow-based models achieved overall Cohen’s kappa scores of 0.511 and 0.452, respectively, while the ensembled model improved the overall kappa to 0.587. The F1-score for the REM stage improved to 0.706 using the ensemble method, compared to 0.685 and 0.532 achieved by the individual models, respectively. In the four-stage classification (Wake, REM, Light, Deep) task, a deep sleep sensitivity of 0.596 was attained through the application of probabilistic ensembling. Conclusions: A fusion scheme of complementary information from the CPAP and PPG enhances the accuracy of sleep stage detection and hence enables more precise sleep monitoring, especially with an improved REM identification. Clinical implications include applying the proposed algorithm to improve in-home auto-CPAP titration by capturing REM-related respiratory instability and avoiding under-titration in REM-dominant OSAHS, better reflecting the patient’s true nocturnal respiratory needs. Full article
(This article belongs to the Special Issue Wearable Technologies and Sensors for Health Monitoring)
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Other

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24 pages, 1389 KB  
Systematic Review
Wearable-Sensor-Based Physical Activity and Sleep in Children with Down Syndrome Aged 0–5 Years: A Systematic Review
by Gilson Borges, Vanessa Moreira and Fabio Bertapelli
Sensors 2025, 25(23), 7278; https://doi.org/10.3390/s25237278 - 29 Nov 2025
Viewed by 3165
Abstract
Wearables enable objective measurement of physical activity (PA) and sleep. Studies that have examined PA and sleep in children with Down syndrome (DS) have not been systematically reviewed. The objectives of this systematic review (PROSPERO: CRD420251036478) were to: (1) describe patterns of PA [...] Read more.
Wearables enable objective measurement of physical activity (PA) and sleep. Studies that have examined PA and sleep in children with Down syndrome (DS) have not been systematically reviewed. The objectives of this systematic review (PROSPERO: CRD420251036478) were to: (1) describe patterns of PA and sleep in children with DS; (2) compare PA and sleep between DS and non-DS; and (3) evaluate sensor data collection procedures. Searches were conducted in PubMed, Scopus, Web of Science, Embase, and SPORTDiscus, with the last search on 7 October 2025. Risk of bias was assessed with the Joanna Briggs Institute tools. From 203 records, 9 original studies were included. Children with DS (n = 8–66 participants; 1–67 months) showed small changes in movement rates over time and greater upper- than lower-limb movements. Segment-specific counts and time spent on high-intensity activity were lower in DS than non-DS. Overall, children with DS exhibited poor sleep quality, sleeping approximately one hour less than controls and 3–7 h below global recommendations. Sensor data collection protocols varied in epoch length (15–30 s), attachment site (wrist, ankle, and hip), and device model. Population-based research employing standardized sensor procedures is warranted to better establish PA levels and sleep quality in children with DS. Full article
(This article belongs to the Special Issue Wearable Technologies and Sensors for Health Monitoring)
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