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Biomedical Electronics and Wearable Systems—2nd Edition
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Biomedical Electronics and Wearable Systems—2nd Edition

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 3890

Special Issue Editor

Dr. José Machado da Silva

E-Mail Website
Guest Editor
Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
Interests: embedded systems; biomedical eletronics; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electronics has become a ubiquitous technology, with new functionalities emerging in all aspects of human life. Apart from smaller integration scales, dense packing, and wider operating characteristics, current electronic technologies allow for developing smarter, more precise, and more reliable systems that provide highly precise diagnostics, personalized therapies, or innovative rehabilitation systems that help toward a better individual health status and improve people’s lives.

This Special Issue aims to bring together a collection of both original research and review papers in the growing field of novel and innovative biomedical electronics and wearable systems, which show how biosciences and engineering are being used to improve biomedical research and technologies that will have an impact on future disease management. The topics to be addressed include, but are not limited to, non-invasive diagnostics, smart prosthetics and patient-specific devices connected to the neuromuscular system, bio-inspired algorithms and on-chip processing for smart portable/implantable sensors, wearables for the acquisition of high-resolution biometric data (biological and physiologic signals, etc.), analog and mixed neuromorphic VLSI, active VLSI implants, neural prostheses, nanomaterials, and tissue–electronic interfaces in implantable systems.

Dr. José Machado da Silva
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-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

  • bioelectronics
  • wearables and smart clothing
  • neuroprosthetics
  • biosensors
  • bionic and biorobotic devices

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Related Special Issue

Published Papers (4 papers)

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Research

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26 pages, 5552 KB  
Article
Fine-Grained Perception for Fundus and Prostate Medical Image Segmentation
by Qiao Ba, Jia-Xuan Jiang, Yuee Li and Zhong Wang
Sensors 2026, 26(9), 2879; https://doi.org/10.3390/s26092879 - 5 May 2026
Viewed by 309
Abstract
Traditional deep learning-based models have achieved promising results in medical image segmentation. However, their performance degrades severely when applied to unseen domains due to variations in imaging protocols, acquisition devices, and patient populations across medical centers, which lead to significant distribution shifts. With [...] Read more.
Traditional deep learning-based models have achieved promising results in medical image segmentation. However, their performance degrades severely when applied to unseen domains due to variations in imaging protocols, acquisition devices, and patient populations across medical centers, which lead to significant distribution shifts. With the emergence of the Segment Anything Model (SAM), a single model now exhibits significantly improved generalization and adaptability to various image types. Nevertheless, while SAM has learned structure representations from large-scale natural images, it lacks fine-grained structural knowledge specific to the medical imaging domain, remaining relatively invariant across imaging domains. In addition, its structural enhancement is vulnerable to unreliable prompts, and patch-wise inference disrupts structural continuity, leading to suboptimal performance in capturing anatomical details. To address this, we propose a novel Medical Fine-grained Segment Anything Model (termed MedFineSAM), which integrates three key modules: shared fine-grained structural enhancement, which extracts and selectively enhances fine-grained structural features shared between prompts and image embeddings via a structural dictionary; a prompt gating mechanism, which estimates prompt confidence and dynamically adjusts prompt weights to avoid erroneous enhancement; and a structural continuity diffusion in frequency domain (SCFD), which performs frequency-domain smoothing during decoding to alleviate structural discontinuity caused by patch aggregation. Experiments on the fundus benchmark and prostate MRI benchmark demonstrate superior generalization performance, offering new insights into leveraging SAM for single-source domain generalization in medical image segmentation. Full article
(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems—2nd Edition)
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16 pages, 1542 KB  
Article
User Authentication Using Inner-Wrist Skin Prints: Feasibility and Performance Assessment with Off-the-Shelf Fingerprint Sensor
by Szymon Cygan, Patryk Lamprecht, Jakub Żmigrodzki, Jan Łusakowski-Milencki, Nikolaos Simopulos, Adrian Zarycki and Piotr Muranty
Sensors 2026, 26(4), 1103; https://doi.org/10.3390/s26041103 - 8 Feb 2026
Viewed by 616
Abstract
Wrist-worn devices enable new paradigms of implicit and continuous user authentication; however, identifying biometric modalities that combine reliability with practical integrability remains challenging. Inner-wrist skin texture represents a relatively unexplored biometric characteristic that may be acquired unobtrusively using commodity hardware. This study evaluates [...] Read more.
Wrist-worn devices enable new paradigms of implicit and continuous user authentication; however, identifying biometric modalities that combine reliability with practical integrability remains challenging. Inner-wrist skin texture represents a relatively unexplored biometric characteristic that may be acquired unobtrusively using commodity hardware. This study evaluates biometric verification based on inner-wrist skin texture using an off-the-shelf capacitive fingerprint sensor and an unmodified, manufacturer-provided fingerprint verification algorithm. Two experiments were conducted. Experiment 1 assessed baseline verification performance under controlled acquisition conditions in a cohort of 33 participants (21 male, 12 female; mean age 30.0 ± 16.9 years, range 10–71 years), yielding 1768 genuine authentication trials. Experiment 2 examined the effect of wrist posture variation under controlled flexion in a separate cohort of 15 participants (11 male, 4 female; mean age 30.9 years, range 18–49 years), with 3900 authentication trials recorded. Across 86,897 impostor comparisons in Experiment 1, no false acceptances were observed, corresponding to a conservative upper bound on the false acceptance rate of 6.7 × 10−5 at the 99.7% confidence level, while the false rejection rate was approximately 2.93%. In Experiment 2, the overall false rejection rate increased to 3.52%, with no clear monotonic relationship between wrist angle and verification performance within the tested range. The results demonstrate that inner-wrist skin texture can be captured and matched using fingerprint-oriented sensing and matching technology under controlled conditions, providing an experimental baseline for this biometric modality. At the same time, the use of a closed matching algorithm and a sensor designed for fingerprints limits interpretability and generalization. These findings motivate further investigation using dedicated recognition methods, larger sensing areas, and extended evaluation protocols tailored specifically to wrist skin print biometrics. Full article
(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems—2nd Edition)
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27 pages, 5664 KB  
Article
An Assessment of the Sensory Function in the Maxillofacial Region: A Dual-Case Pilot Study
by João Maia Aguiar, José Machado da Silva, Carlos Fonseca and Jorge Marinho
Sensors 2025, 25(11), 3355; https://doi.org/10.3390/s25113355 - 26 May 2025
Viewed by 1459
Abstract
Trigeminal somatosensory-evoked potentials (TSEPs) provide valuable insight into neural responses to oral stimuli. This study investigates TSEP recording methods and their impact on interpreting results in clinical settings to improve the development process of neurostimulation-based therapies. The experiments and results presented here aim [...] Read more.
Trigeminal somatosensory-evoked potentials (TSEPs) provide valuable insight into neural responses to oral stimuli. This study investigates TSEP recording methods and their impact on interpreting results in clinical settings to improve the development process of neurostimulation-based therapies. The experiments and results presented here aim at identifying appropriate stimulation characteristics to design an active dental prosthesis capable of contributing to restoring the lost neurosensitive connection between the teeth and the brain. Two methods of TSEP acquisition, traditional and occluded, were used, each conducted by a different volunteer. Traditional TSEP acquisition involves stimulation at different sites with varying parameters to achieve a control base. In contrast, occluded TSEPs examine responses acquired under low- and high-force bite conditions to assess the influence of periodontal mechanoreceptors and muscle activation on measurements. Traditional TSEPs demonstrated methodological feasibility with satisfactory results despite a limited subject pool. However, occluded TSEPs presented challenges in interpreting results, with responses deviating from expected norms, particularly under high force conditions, due to the simultaneous occurrence of stimulation and dental occlusion. While traditional TSEPs highlight methodological feasibility, the occluded approach highlights complexities in outcome interpretation and urges caution in clinical application. Previously unreported results were achieved, which underscores the importance of conducting further research with larger sample sizes and refined protocols in order to strengthen the reliability and validity of TSEP assessments. Full article
(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems—2nd Edition)
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Review

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19 pages, 1193 KB  
Review
Tactical-Grade Wearables and Authentication Biometrics
by Fotios Agiomavritis and Irene Karanasiou
Sensors 2026, 26(3), 759; https://doi.org/10.3390/s26030759 - 23 Jan 2026
Viewed by 920
Abstract
Modern battlefield operations require wearable technologies to operate reliably under harsh physical, environmental, and security conditions. This review looks at today and tomorrow’s potential for ready field-grade wearables embedded with biometric authentication systems. It details physiological, kinematic, and multimodal sensor platforms built to [...] Read more.
Modern battlefield operations require wearable technologies to operate reliably under harsh physical, environmental, and security conditions. This review looks at today and tomorrow’s potential for ready field-grade wearables embedded with biometric authentication systems. It details physiological, kinematic, and multimodal sensor platforms built to withstand rugged, high-stress environments, and reviews biometric modalities like ECG, PPG, EEG, gait, and voice for continuous or on-demand identity confirmation. Accuracy, latency, energy efficiency, and tolerance to motion artifacts, environmental extremes, and physiological variability are critical performance drivers. Security threats, such as spoofing and data tapping, and techniques for template protection, liveness assurance, and protected on-device processing also come under review. Emerging trends in low-power edge AI, multimodal integration, adaptive learning from field experience, and privacy-preserving analytics in terms of defense readiness, and ongoing challenges, such as gear interoperability, long-term stability of templates, and common stress-testing protocols, are assessed. In conclusion, an R&D plan to lead the development of rugged, trustworthy, and operationally validated wearable authentication systems for the current and future militaries is proposed. Full article
(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems—2nd Edition)
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