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Intelligent Sensing Technologies to Facilitate Clinical/Medical Decision Making
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Intelligent Sensing Technologies to Facilitate Clinical/Medical Decision Making

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1440

Special Issue Editors

Prof. Dr. Peter W. McCarthy

E-Mail Website
Guest Editor
1. Faculty of Life Science and Education, University of South Wales, Treforest, Pontypridd CF37 1DL, UK
2. Faculty of Health Sciences, Durban University of Technology, Durban 1334, South Africa
Interests: use of technology to protect athletes, replace/enhance impaired nervous systems, and facilitate restoration of function
Special Issues, Collections and Topics in MDPI journals
Dr. Vincenzo Cascioli

E-Mail Website
Guest Editor
Independent Researcher, Perth, WA 6164, Australia
Interests: evaluation of sitting comfort and discomfort; signal measurement at the user–seat interface
Prof. Dr. Zhuofu Liu

E-Mail Website
Guest Editor
The Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China
Interests: data analysis; signal measurement and detection; medical information processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid evolution of intelligent systems and sensing technologies, healthcare is experiencing a paradigm shift in clinical decision making. This Special Issue focuses on the integration of sensing technologies—such as wearable biosensors, Internet of Things (IoT)-enabled sensing devices, medical imaging sensors, and ambient sensing systems—with intelligent algorithms (e.g., machine learning, deep learning, and regression analysis) to enhance the accuracy, timeliness, and personalization of medical decisions. Sensing technologies serve as the critical data acquisition infrastructure for clinical intelligence, capturing real-time physiological signals, pathological markers, and environmental cues that form the foundation of data-driven diagnosis, treatment planning, and prognostic assessment. By bridging sensing innovations and intelligent analytics, this issue showcases original research, reviews, and case studies that address key challenges (e.g., sensor data noise reduction, real-time processing, and clinical validation) and highlight transformative applications—from remote patient monitoring for chronic disease management to AI-driven diagnostic and prognostic models. We aim to gather interdisciplinary insights from engineering, computer science, and clinical medicine; integrate these technologies into healthcare workflows; and advance the synergy between sensing and artificial intelligence to revolutionize clinical practice.

Prof. Dr. Peter W. McCarthy
Dr. Vincenzo Cascioli
Prof. Dr. Zhuofu Liu
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

  • intelligent sensing
  • clinical decision making
  • wearable biosensors
  • medical image processing
  • machine learning in healthcare
  • sensing-driven diagnosis
  • real-time clinical monitoring
  • artificial intelligence
  • clinical sample analysis and validation

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Published Papers (2 papers)

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Research

20 pages, 4120 KB  
Article
An Efficient Finger Vein Recognition Method Based on Improved Lightweight MobileNet
by Xuhui Zhang, Yuxi Liu, Yixin Yan, Jiabin Li and Lei Xu
Sensors 2026, 26(5), 1634; https://doi.org/10.3390/s26051634 - 5 Mar 2026
Viewed by 545
Abstract
Finger vein recognition has emerged as a highly robust and intrinsically stable biometric technology, demonstrating great potential in identity authentication and intelligent security applications. However, conventional methods still suffer from constraints in feature representation and computational efficiency, particularly under challenging conditions such as [...] Read more.
Finger vein recognition has emerged as a highly robust and intrinsically stable biometric technology, demonstrating great potential in identity authentication and intelligent security applications. However, conventional methods still suffer from constraints in feature representation and computational efficiency, particularly under challenging conditions such as illumination variation, pose deviation, and noise interference. To address these challenges, this study presents an efficient finger vein recognition approach based on a lightweight convolutional neural network (LCNN) architecture. The proposed framework integrates a multi-stage image preprocessing pipeline for automatic vein region detection, advanced denoising, and refined texture enhancement, which is subsequently followed by compact feature modeling within a lightweight deep network. Extensive experiments on the public Shandong University Machine Learning and Applications-Homologous Multi-Modal Traits (SDUMLA-HMT) dataset and a self-acquired Laboratory Finger-Vein (Lab-Vein) dataset validate the superiority of the proposed method, achieving recognition accuracies of 97.1% and 98.3%, respectively, surpassing existing benchmark models. Moreover, the model demonstrates notable reductions in parameter complexity and computational cost, achieving an average inference time of only 12.6 ms, which confirms its strong real-time capability and suitability for embedded deployment. Overall, the proposed approach attains a desirable trade-off between accuracy and efficiency, offering meaningful implications for the advancement of lightweight biometric recognition systems. Full article
(This article belongs to the Special Issue Intelligent Sensing Technologies to Facilitate Clinical/Medical Decision Making)
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15 pages, 2322 KB  
Article
Continuous Accelerometry-Based Tremor Detection During Daily Living
by Luis Martinez, Orlando Martinez, Stephen L. Schmidt, Rocio Rodriguez Capilla, Hector Gardea, Arabo Gholian, Dennis A. Turner and Deborah Soonmee Won
Sensors 2026, 26(5), 1459; https://doi.org/10.3390/s26051459 - 26 Feb 2026
Viewed by 572
Abstract
As a step towards advancing adaptive DBS control for Parkinson’s disease, we have developed an automated algorithm that detects tremor continuously on a seconds-resolution time scale from a wearable accelerometer and present the feasibility study test results. Triaxial acceleration data were wirelessly streamed [...] Read more.
As a step towards advancing adaptive DBS control for Parkinson’s disease, we have developed an automated algorithm that detects tremor continuously on a seconds-resolution time scale from a wearable accelerometer and present the feasibility study test results. Triaxial acceleration data were wirelessly streamed from an Apple Watch as well as acquired from an internal accelerometer in the implanted DBS device itself. The algorithm first determines if there is any high-power voluntary activity, such as walking, using the arm, or transitioning from sitting to standing; then, it identifies peaks in the 4–7 Hz Parkinsonian tremor frequency band. Peak detection for tremor activity was more accurate using the Apple Watch than the IPG. Peak and harmonic detection were also more accurate using continuous wavelet transforms than short-time Fourier transform. According to the repeated measures correlation, our detection algorithm correlated strongly with DBS intensity (Subject RZCH: r = −0.93, p = 3.6 × 10−5; 6KOZ: r = −0.97, p = 1.6 × 10−5, NU5U: r = −0.94, p = 0.057). Pearson’s correlation coefficient between our tremor detection algorithm and the currently accepted industry metric was found to be 0.57 (t-value = 8.5, dof = 148, p < 1 × 10−6). Our algorithm is distinctive in the capability to detect Parkinsonian tremor, with a high degree of clinical relevance, during daily living activities and is able to discriminate tremor from walking, using a convenient, commercial wrist-worn accelerometer. Full article
(This article belongs to the Special Issue Intelligent Sensing Technologies to Facilitate Clinical/Medical Decision Making)
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