Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.8
5.6
Journals
Biosensors
Special Issues
Latest Wearable Biosensors—2nd Edition
share announcement

Latest Wearable Biosensors—2nd Edition

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Wearable Biosensors".

Deadline for manuscript submissions: 25 July 2026 | Viewed by 4621

Special Issue Editors

Prof. Dr. Donald Y.C. Lie

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
Interests: low−power RF/analog integrated circuits & System−on−a−Chip (SoC) design and test; interdisciplinary research on medical electronics, biosensors & biosignal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Tam Q. Nguyen

E-Mail Website
Guest Editor
Department of Otolaryngology, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
Interests: head and neck surgery; speech and hearing sciences
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chungchih Hung

E-Mail Website
Guest Editor
Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
Interests: analog and mixed-signal IC design; biomedical IC design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

After the successful publication of the first edition of the Special Issue "Latest Wearable Biosensors", we are pleased to announce the exciting second edition! Facing a rapidly aging global population, the future of healthcare will need to become heavily personalized to reduce its enormous associated costs and become “smart”. With this, clinicians need to have certain continuously monitored biomarkers for evaluating patients’ health conditions individually. The topic of wearable sensors has attracted much more attention recently; with the rapid development of several key advanced technologies, wearable biosensors are now able to provide practical real-time monitoring of patients’ health and well-being. Wearable biosensors can be used to track vital signs, steps, sleep, stress, hydration, oxygen content and glucose levels in the blood, amongst other measures. Furthermore, they can be easily connected wirelessly to cellphone applications, making them user friendly, inexpensive, and ubiquitous. Research and development on smart wearable biosensors for personalized e-Health services has been conducted around the world. On this note, the rapid worldwide development and public embrace of wearable biosensors (e.g., Fitbit™ and various smart watches) supports the need for another dedicated Special Issue in this area. These wearable systems for health monitoring may feature different types of miniature biosensors (say, printable and wearable patches) capable of measuring different physiological parameters: vital signs (heart rate, respiration rate, body and skin temperature, oxygen saturation, etc.), skin conductance, blood pressure, electrocardiogram (ECG) measurements, electroencephalography (EEG) measurements, acceleration, and rotation. Furthermore, one can also use the measured data to predict/assess mood changes, cardio health, fitness conditions, posture, cognition state, sleep quality, fall detection, seizure prediction, and for various other monitoring purposes to improve subjects' quality of life, health management, disease control status, and even survival rates from emergency rescue operators. With the latest breakthroughs in AI (artificial intelligence), one can expect the mass production of long-lasting, accurate, robust and smart wearable biosensors with a high sensitivity and selectivity. These may hold the key to replacing many traditional diagnostic medical devices to enable timely personalized e-Health treatment and intervention, thus reducing healthcare costs and delivering a higher quality of life and well-being for patients.

Prof. Dr. Donald Y.C. Lie
Dr. Tam Q. Nguyen
Prof. Dr. Chungchih Hung
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. Biosensors is an international peer-reviewed open access monthly 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 2200 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

  • accelerometer activity biosensors
  • electrocardiogram (ECG)
  • electroencephalography (EEG)
  • mobile phone monitoring
  • neuroimaging skin conductance
  • smart phone
  • smart sensor
  • vital signs
  • wearable biosensors
  • wearable sensors

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

16 pages, 3497 KB  
Article
Utilizing Circadian Heart Rate Variability Features and Machine Learning for Estimating Left Ventricular Ejection Fraction Levels in Hypertensive Patients: A Composite Multiscale Entropy Analysis
by Nanxiang Zhang, Qi Pan, Shuo Yang, Leen Huang, Jianan Yin, Hai Lin, Xiang Huang, Chonglong Ding, Xinyan Zou, Yongjun Zheng and Jinxin Zhang
Biosensors 2025, 15(7), 442; https://doi.org/10.3390/bios15070442 - 10 Jul 2025
Viewed by 1520
Abstract
Background: Early identification of left ventricular ejection fraction (LVEF) levels during the progression of hypertension is essential to prevent cardiac deterioration. However, achieving a non-invasive, cost-effective, and definitive assessment is challenging. It has prompted us to develop a comprehensive machine learning framework for [...] Read more.
Background: Early identification of left ventricular ejection fraction (LVEF) levels during the progression of hypertension is essential to prevent cardiac deterioration. However, achieving a non-invasive, cost-effective, and definitive assessment is challenging. It has prompted us to develop a comprehensive machine learning framework for the automatic quantitative estimation of LVEF levels from electrocardiography (ECG) signals. Methods: We enrolled 200 hypertensive patients from Zhongshan City, Guangdong Province, China, from 1 November 2022 to 1 January 2025. Participants underwent 24 h Holter monitoring and echocardiography for LVEF estimation. We developed a comprehensive machine learning framework that initiated with preprocessed ECG signal in one-hour intervals to extract CMSE-based heart rate variability (HRV) features, then utilized machine learning models such as linear regression (LR), Support Vector Machines (SVMs), and random forests (RFs) with recursive feature elimination for optimal LVEF estimation. Results: The LR model, notably during early night interval (20:00–21:00), achieved a RMSE of 4.61% and a MAE of 3.74%, highlighting its superiority. Compared with other similar studies, key CMSE parameters (Scales 1, 5, Slope 1–5, and Area 1–5) can effectively enhance regression models’ estimation performance. Conclusion: Our findings suggest that CMSE-derived circadian HRV features from Holter ECG could serve as a non-invasive, cost-effective, and interpretable solution for LVEF assessment in community settings. From a machine learning interpretable perspective, the proposed method emphasized CMSE’s clinical potential in capturing autonomic dynamics and cardiac function fluctuations. Full article
(This article belongs to the Special Issue Latest Wearable Biosensors—2nd Edition)
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

32 pages, 8493 KB  
Review
From Single-Ion to Integrated Multi-Ion Platforms: Wearable Sweat Sensors for Electrolyte Monitoring
by Jieru Yang, Junyao Li, Xiao Han, Zewen Wei, Gang Wang and Ting Zou
Biosensors 2026, 16(6), 317; https://doi.org/10.3390/bios16060317 - 1 Jun 2026
Abstract
Sweat contains abundant ions, offering a rich source of physiological information for non-invasive health monitoring. Wearable sweat sensors have become a promising technology due to advances in electrochemical devices, sensing materials and structural design. The current monitoring platforms primarily employ two fundamental sensing [...] Read more.
Sweat contains abundant ions, offering a rich source of physiological information for non-invasive health monitoring. Wearable sweat sensors have become a promising technology due to advances in electrochemical devices, sensing materials and structural design. The current monitoring platforms primarily employ two fundamental sensing modalities to convert sweat chemical information into detectable numerical signals: electrochemical (potentiometric, voltammetric, transistor-based) and optical (colorimetric) transduction mechanisms. The demand for more comprehensive physiological and biochemical data in clinical diagnosis and daily health monitoring is driving sensors towards multi-ion detection. Building on these modalities, researchers have optimized hardware and software algorithms based on the characteristics of different ions, thereby promoting the transition of wearable devices from the laboratory to practical applications. Here, we summarize recent progress in wearable sweat ion sensors, focusing on their mechanisms, advantages, and limitations. Finally, current challenges and future prospects of wearable sweat ion sensors for research applications, clinical use, and market demands are discussed. Full article
(This article belongs to the Special Issue Latest Wearable Biosensors—2nd Edition)
►▼ Show Figures

Figure 1

Other

Jump to: Research, Review

23 pages, 581 KB  
Systematic Review
Advances in AI-Driven EEG Analysis for Neurological and Oculomotor Disorders: A Systematic Review
by Faisal Mehmood, Sajid Ur Rehman, Asif Mehmood and Young-Jin Kim
Biosensors 2026, 16(1), 15; https://doi.org/10.3390/bios16010015 - 24 Dec 2025
Cited by 4 | Viewed by 2424
Abstract
Electroencephalography (EEG) has emerged as a powerful, non-invasive modality for investigating neurological and oculomotor disorders, particularly when combined with advances in artificial intelligence (AI). This systematic review examines recent progress in machine learning (ML) and deep learning (DL) techniques applied to EEG-based analysis [...] Read more.
Electroencephalography (EEG) has emerged as a powerful, non-invasive modality for investigating neurological and oculomotor disorders, particularly when combined with advances in artificial intelligence (AI). This systematic review examines recent progress in machine learning (ML) and deep learning (DL) techniques applied to EEG-based analysis for the diagnosis, classification, and monitoring of neurological conditions, including oculomotor-related disorders. Following the PRISMA guidelines, a structured literature search was conducted across major scientific databases, resulting in the inclusion of 15 peer-reviewed studies published over the last decade. The reviewed works encompass a range of neurological and ocular-related disorders and employ diverse AI models, from conventional ML algorithms to advanced DL architectures capable of learning complex spatiotemporal representations of neural signals. Key trends in feature extraction, signal representation, model design, and validation strategies are synthesized here to highlight methodological advancements and common challenges. While the reviewed studies demonstrate the growing potential of AI-enhanced EEG analysis for supporting clinical decision-making, limitations such as small sample sizes, heterogeneous datasets, and limited external validation remain prevalent. Addressing these challenges through standardized methodologies, larger multi-center datasets, and robust validation frameworks will be essential for translating EEG-driven AI approaches into reliable clinical applications. Overall, this review provides a comprehensive overview of current methodologies and future directions for AI-driven EEG analysis in neurological and oculomotor disorder assessment. Full article
(This article belongs to the Special Issue Latest Wearable Biosensors—2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop