Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
12.1
6.2
Journals
Biosensors
Special Issues
Biosensors for Physiological Signal Monitoring
share announcement

Biosensors for Physiological Signal Monitoring

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2635

Editors

Dr. Guangli Li

E-Mail Website
Guest Editor
Hunan Key Laboratory of Biomedical Nanomaterials and Devices, School of Biological Science and Medical Engineering, Hunan University of Technology, Zhuzhou 412007, China
Interests: biopotential sensors; brain–computer interfaces; electrochemical sensor; flexible sensors
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xueye Chen

E-Mail Website
Guest Editor
School of Transportation, Ludong University, Yantai 264025, China
Interests: micro/nano manufacturing; flexible mems sensors; microfluidic systems
Special Issues, Collections and Topics in MDPI journals
Dr. Bowen Ji

E-Mail Website
Guest Editor
Unmanned System Research Institue, Northwestern Polytechnical University, Xi’an, China
Interests: brain–computer implantable brain interface devices; wearable flexible electronic devices; human–computer interaction and intelligent perception
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The convergence of flexible electronics and biomarker sensing has revolutionized continuous health monitoring. Wearable biosensors now enable multimodal tracking of electrophysiological signals (i.e., EEG, ECG, EMG, EOG, and EDA), biomechanical parameters (i.e., strain and pressure), and biochemical analytes (i.e., glucose, lactate, cortisol, and pH). These platforms provide unprecedented insights into physiological states, from cardiovascular health to emotional stress and cognitive fatigue.

This Special Issue highlights innovations in materials, device architectures, and signal processing that advance epidermal sensing systems. We prioritize solutions addressing critical wearable challenges: motion artifact suppression, skin-interfacing reliability, and long-term biosignal fidelity. Emerging technologies integrating machine learning for real-time health diagnostics are of particular interest.

We invite contributions on the following:

  • Novel flexible substrates and electrodes for robust biosignal acquisition;
  • Multiplexed sensor arrays for concurrent physical or biochemical monitoring;
  • Energy-efficient wireless interfaces and edge-computing architectures;
  • Anti-fouling coatings and biocompatible adhesion strategies;
  • Fusion algorithms and AI models for cross-modal physiological data interpretation;
  • Clinical validation studies of wearable health prediction models;
  • Biosensors for brain–computer interfaces, human–machine interactions and health monitoring.

Both fundamental research and translational applications are welcome, with an emphasis on scalable manufacturing and user-centric design.

Dr. Guangli Li
Prof. Dr. Xueye Chen
Dr. Bowen Ji
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-anonymized 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

  • wearable sensor
  • flexible electrode
  • physiological signal
  • biochemical monitoring
  • machine learning
  • brain–computer interfaces
  • health monitoring

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 (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

27 pages, 1221 KB  
Article
Digital and Remote Interventions for Musculoskeletal Aging: Real-Time Muscle Strain Severity Detection Using Artificial Intelligence
by Zulaikha Fatima, Abdullah, Nida Hafeez, Rolando Quintero Téllez, Miguel Jesús Torres Ruiz, Carlos Guzmán Sánchez Mejorada, Miguel Félix Mata-Rivera and Roberto Zagal-Flores
Biosensors 2026, 16(7), 354; https://doi.org/10.3390/bios16070354 - 25 Jun 2026
Viewed by 412
Abstract
As global populations grow and technology advances, daily life is increasingly shaped by digital systems such as computers and smart devices. However, prolonged device use has contributed to increasing physical and mental health concerns, particularly those associated with poor sitting posture. Posture-related strain [...] Read more.
As global populations grow and technology advances, daily life is increasingly shaped by digital systems such as computers and smart devices. However, prolonged device use has contributed to increasing physical and mental health concerns, particularly those associated with poor sitting posture. Posture-related strain is frequently overlooked and contributes to musculoskeletal discomfort, including back, neck, shoulder, and wrist pain, and may also be associated with sleep disturbances and elevated stress levels. To the best of our knowledge and based on the existing literature, this is the first study to introduce a machine learning-based framework for advanced muscle strain severity classification using Internet of Things (IoT) devices that integrates posture monitoring and muscle strain detection into a unified low-cost framework ($23 hardware cost). The primary objective of this work is accurate classification of muscle strain severity, while real-time alerts serve as a secondary ergonomic feedback mechanism. Specifically, this study makes four major contributions. First, we created a novel dataset through real-time acquisition of electromyography (EMG) and posture signals from participants in hospital and industrial environments, capturing diverse muscle strain patterns validated against clinical assessment procedures. Second, we designed a two-part hardware architecture consisting of posture detection (PD) and strain detection (SD) modules using a NodeMCU ESP8266, HC-SR04 ultrasonic sensor, EMG sensor, and buzzer for real-time physiological monitoring, incorporating EMG-specific preprocessing including band-pass filtering, rectification, and RMS smoothing. Third, we proposed and evaluated a hybrid machine learning framework integrating Vision Transformer (ViT) and XGBoost to classify strain severity into three study-specific categories: baseline (EMG RMS < 40 µV), compensatory strain (40–59 µV), and overload (≥60 µV). These categories were used as reproducible severity proxies for machine learning annotation and should not be interpreted as universal biomarkers of structural tissue damage. Finally, the proposed framework achieved a classification accuracy of 99.0% (95% CI: 98.5–99.5%) with an inference latency of 15.2 ms. Full article
(This article belongs to the Special Issue Biosensors for Physiological Signal Monitoring)
►▼ Show Figures

Figure 1

33 pages, 5647 KB  
Article
Integration of Machine Learning Techniques in ECG-Based Multiclass Arrhythmia Classification with Explainability Analysis
by Abdullah, Zulaikha Fatima, Abdollah Abadian, Carlos Guzmán Sánchez Mejorada, Miguel Jesús Torres Ruiz and Rolando Quintero Téllez
Biosensors 2026, 16(6), 326; https://doi.org/10.3390/bios16060326 - 3 Jun 2026
Viewed by 809
Abstract
Electrocardiogram (ECG) analysis is a cornerstone non-invasive diagnostic technique for detecting cardiac arrhythmias, which remain a leading cause of mortality worldwide. While recent advances in deep learning have significantly improved automated arrhythmia classification, the current literature lacks systematic, fair comparisons of fundamental neural [...] Read more.
Electrocardiogram (ECG) analysis is a cornerstone non-invasive diagnostic technique for detecting cardiac arrhythmias, which remain a leading cause of mortality worldwide. While recent advances in deep learning have significantly improved automated arrhythmia classification, the current literature lacks systematic, fair comparisons of fundamental neural architectures under unified experimental conditions, and very few studies provide model interpretability. This study addresses these gaps by first providing a rigorous comparative analysis of three representative architectures—Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Residual Network (ResNet)—on the MIT-BIH Arrhythmia Database under identical preprocessing, training, and evaluation protocols. We then propose an efficient Fine-Tuned CNN (FT-CNN) optimized for ECG signal characteristics through adaptive kernel sizing for P-QRS-T morphological extraction, multi-faceted regularization including L2, dropout, and batch normalization, cosine annealing learning rate, and a custom loss function combining weighted categorical cross-entropy with focal loss with gamma equal to 2.0 to address severe class imbalance. The FT-CNN achieves an accuracy of 98.51%, outperforming fourteen benchmark models, including standard CNN with an accuracy of 97.20%, ResNet with 96.88%, LSTM with 96.50%, GRU with 96.30%, and traditional classifiers. Comprehensive ablation studies confirm an improvement of 6.17% over the baseline. Class-wise analysis reveals excellent performance for normal beats with an F1-score of 0.99, ventricular ectopic beats with 0.95, and unknown beats with 0.98, while supraventricular ectopic beats with an F1-score of 0.79 and fusion beats with 0.70 remain challenging. Unlike most prior works, we integrate Grad-CAM and Integrated Gradients for explainability, quantitatively evaluating attribution faithfulness, sanity checks, and noise robustness. Full article
(This article belongs to the Special Issue Biosensors for Physiological Signal Monitoring)
►▼ Show Figures

Figure 1

28 pages, 14925 KB  
Article
State-Referenced Truncated SVD for Dynamic Microwave Monitoring of Intracranial Hemorrhage
by Zekun Zhang, Heng Liu, Ruide Li, Huiyuan Zhu, Fan Li, Shujun Ni, Aojun Liu and Yao Zhai
Biosensors 2026, 16(5), 285; https://doi.org/10.3390/bios16050285 - 14 May 2026
Viewed by 829
Abstract
Microwave imaging is a promising non-ionizing technique for bedside follow-up of intracranial hemorrhage, but dynamic monitoring remains challenging under limited multistatic sampling because weak inter-frame changes can be obscured by measurement variability, model mismatch, and the high cost of frame-by-frame nonlinear inversion. To [...] Read more.
Microwave imaging is a promising non-ionizing technique for bedside follow-up of intracranial hemorrhage, but dynamic monitoring remains challenging under limited multistatic sampling because weak inter-frame changes can be obscured by measurement variability, model mismatch, and the high cost of frame-by-frame nonlinear inversion. To address this problem, this paper proposes a state-referenced truncated singular-value decomposition (SR-TSVD) framework for dynamic microwave monitoring of hemorrhagic evolution. The method maintains an internal gate state and reconstructs only the state-referenced increment at each monitoring instant. A row-whitened TSVD inversion is introduced to reduce channel dominance effects and improve robustness to route-dependent imbalance, while a residual-driven gate-refresh mechanism updates the internal state only when the current linearization background becomes insufficiently accurate. The proposed method was validated through two-dimensional numerical experiments and hardware phantom measurements. The numerical study examined different lesion evolution scenarios and analyzed the effects of antenna count, frequency diversity, and measurement noise. The hardware study showed that the method preserves the main dynamic evolution in a real measurement system and remains more stable than baseline linear methods under sparse array conditions. These results indicate that SR-TSVD provides an effective and computationally practical framework for repeated bedside microwave monitoring of intracranial hemorrhage. Full article
(This article belongs to the Special Issue Biosensors for Physiological Signal Monitoring)
►▼ 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