Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Sensor for Biomedical and Machine Learning Applications
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sensor for Biomedical and Machine Learning Applications

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1334

Special Issue Editors

Prof. Dr. Georgina Beltrán-Pérez

E-Mail Website
Guest Editor
Facultad de Ciencias Físico Matemáticas, Benemérita Universidad Autónoma de Puebla, Puebla, Mexico
Interests: optical fibers; sensors; machine learning; pathology detection; sensing materials; breath analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Severino Muñoz Aguirre

E-Mail Website
Guest Editor
Facultad de Ciencias Físico Matemáticas, Benemérita Universidad Autónoma de Puebla, Puebla, Mexico
Interests: sensors; breath analysis; electronic noses; optical fibers; machine learning; sensing materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of sensors is fundamental to technological, social and even economic progress. Its importance lies in its ability to improve industrial processes, optimize resources, monitoring and diagnostics in the health and environmental area, as well as enabling emerging technologies.

This Special Issue aims to showcase the latest advancements in the field of sensor technology, with a focus on biomedical and machine learning applications. We invite researchers, engineers, and industry professionals to submit their original research articles and reviews on topics such optical fibers, for sensors.

Topics of interest include but are not limited to novel sensors, functionalization of materials for specific sensing applications, design and fabrication of sensors using advanced materials, and the integration of sensors into wearable devices and Machine Learning systems.

Prof. Dr. Georgina Beltrán-Pérez
Dr. Severino Muñoz Aguirre
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

  • biomedical
  • machine learning
  • novel 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.

Published Papers (2 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

18 pages, 3416 KB  
Article
Early Drowsiness Detection via Second-Order Derivative Analysis of Heart Rate Variability: A Non-Contact ECG Approach with Machine Learning
by Fabrice Vaussenat, Abhiroop Bhattacharya, Julie Payette, Alireza Saidi, Victor Bellemin, Geordi-Gabriel Renaud-Dumoulin, Sylvain G. Cloutier and Ghyslain Gagnon
Sensors 2026, 26(4), 1348; https://doi.org/10.3390/s26041348 - 20 Feb 2026
Viewed by 569
Abstract
Drowsy driving contributes to roughly 20% of traffic fatalities, yet most detection systems rely on behavioral cues that appear only after impairment has set in. Here we ask whether first and second derivatives of heart rate variability (HRV) can detect pre-crash states earlier [...] Read more.
Drowsy driving contributes to roughly 20% of traffic fatalities, yet most detection systems rely on behavioral cues that appear only after impairment has set in. Here we ask whether first and second derivatives of heart rate variability (HRV) can detect pre-crash states earlier than conventional approaches. Twenty-five participants completed 49 driving simulator sessions while we recorded cardiac activity through capacitive ECG electrodes embedded in the seat backrest—a non-contact method that avoids the privacy concerns of camera-based monitoring. To prevent circular evaluation, ground truth labels were based solely on crash proximity rather than HRV-derived scores. The combined HRV feature set (conventional metrics plus derivatives) achieved AUC = 0.863 for pre-crash prediction; derivatives alone reached only AUC = 0.573, indicating their value as complementary rather than standalone features. Driving performance indicators remained the strongest predictors (AUC = 0.999). Temporally, derivative-based detection preceded behavioral manifestations by 5–8 min and crash events by 6.8 ± 2.3 min. Across 1591 crashes and 6.78 million data points, we found that HRV derivatives capture physiological changes that precede overt impairment, though their utility depends on integration with other feature types. Full article
(This article belongs to the Special Issue Sensor for Biomedical and Machine Learning Applications)
►▼ Show Figures

Figure 1

14 pages, 3673 KB  
Article
Use of the Feature Scaling and Machine Learning Techniques on Optical Fiber Biosensors for the Detection of Neuroprotector IL-10 in Serum of a Murine Model with Cerebral Ischemia
by R. I. Bandala-Daniel, L. Ocelotl-Zayas, R. Delgado-Macuil, K. González-León, M. García-Juárez, S. Muñoz-Aguirre, J. Castillo-Mixcóatl and G. Beltrán-Pérez
Sensors 2026, 26(4), 1174; https://doi.org/10.3390/s26041174 - 11 Feb 2026
Cited by 1 | Viewed by 438
Abstract
Typically, response analysis of optical fiber biosensors focuses on changes in amplitude and wavelength shifts in the biosensor spectrum; therefore, not all of the spectral range is used for this analysis. On the other hand, if the entire spectrum is used, it is [...] Read more.
Typically, response analysis of optical fiber biosensors focuses on changes in amplitude and wavelength shifts in the biosensor spectrum; therefore, not all of the spectral range is used for this analysis. On the other hand, if the entire spectrum is used, it is possible to leverage the current data in the spectrum and thus improve the performance of the biosensor. To do this, it is necessary to analyze a large amount of data present in each measured spectrum. This task can be made easier by using dimensionality reduction techniques. In addition, it is necessary to establish which spectral regions provide relevant information. Scaling techniques are mathematical data preprocessing tools used in machine learning to adjust the numerical scale of variables so that they have comparable weight and even highlight those characteristics that provide more information. To our knowledge, the use of these techniques in the development of optical fiber biosensors is not very common, which is why we believe they represent an attractive topic of study in this area. With the help of scaling techniques, we can modify the scale of the data so that all the information contained in the spectrum is used, regardless of its magnitude. In this work, two biosensors based on a chirped long period fiber grating (CLPFG) and a chirped Mach–Zehnder interferometer (CMZI) were developed for the detection of interleukin-10 (IL-10). Principal component analysis (PCA) was used as a dimensionality reduction technique together with a support vector machine (SVM) classifier with four different scaling techniques, standardization, minimum–maximum scaling, robust scaling, and a custom transformer, to compare the IL-10 detection performance of the biosensors. The results showed that robust scaling in CMZI performed best in detecting IL-10, with an F1-score equal to 1, as well as better reliability in detecting the protein. Full article
(This article belongs to the Special Issue Sensor for Biomedical and Machine Learning Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop