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Biophysica
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Medical Physics and Physiological Measurements: Intelligent Biosensing, Wearables and AI-Driven...
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Medical Physics and Physiological Measurements: Intelligent Biosensing, Wearables and AI-Driven Quantified Health

A special issue of Biophysica (ISSN 2673-4125).

Deadline for manuscript submissions: 30 November 2026 | Viewed by 770

Editor

Prof. Dr. Constantinos Koutsojannis

E-Mail Website
Guest Editor
Department of Physiotherapy, Laboratory of Health Physics and Computational Intelligence, School of Health Rehabilitation Sciences, University of Patras, 25100 Aιgio, Greece
Interests: non-ionizing radiation protection in medicine; medical physics; health physics; bio-signals; electrophysiology; medical error; intelligent medical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in medical physics, physiological sensing technologies, and data-centric computational sciences are accelerating the transition toward scalable, precision, and preventive healthcare. This Special Issue aims to attract high-impact, forward-looking contributions that address emerging scientific and technological challenges in the measurement, modeling, and intelligent interpretation of human physiological processes. We particularly encourage submissions that demonstrate strong methodological novelty, large-scale validation, translational relevance, and potential for clinical, industrial, or societal impact.

Topics of interest include next-generation biosignal acquisition and analysis, wearable and remote monitoring systems, digital biomarkers, and multimodal physiological data fusion. Special emphasis is placed on artificial intelligence approaches such as deep learning, foundation models for biosignals, federated and privacy-preserving learning, explainable and trustworthy AI, and real-time predictive analytics. Contributions integrating physics-based modeling, digital twin ecosystems, uncertainty quantification, and hybrid mechanistic–data-driven frameworks are highly welcomed, particularly when enabling personalized diagnosis, adaptive rehabilitation, or closed-loop therapeutic interventions.

We also invite studies addressing implementation science, interoperability, standardization of physiological measurement protocols, and deployment in real-world environments, including telemedicine, home-based care, sports performance, and occupational health. By bringing together interdisciplinary perspectives, this Special Issue aims to define future directions in intelligent physiological measurement and to promote reproducible, data-intensive research that can shape next-generation digital health and precision medicine paradigms.

Prof. Dr. Constantinos Koutsojannis
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. Biophysica 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 1200 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

  • medical physics
  • physiological sensing
  • biosignals
  • digital biomarkers
  • wearable and remote monitoring
  • artificial intelligence
  • deep learning
  • foundation models
  • federated learning
  • digital twins
  • hybrid modeling
  • multimodal data fusion
  • telehealth
  • precision medicine
  • rehabilitation engineering

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Published Papers (2 papers)

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Research

19 pages, 2556 KB  
Article
Comparing Brain and Electrodermal Responses for Arousal Classification in Human–Computer Interaction
by Yedukondala Rao Veeranki, Luis R. Mercado-Diaz and Hugo F. Posada-Quintero
Biophysica 2026, 6(3), 49; https://doi.org/10.3390/biophysica6030049 - 8 Jun 2026
Viewed by 136
Abstract
Emotion recognition (ER) in human–computer interaction (HCI) holds immense potential for real-world applications, but traditional approaches based on electroencephalography (EEG) face challenges due to the complexity and impracticality of collecting and analyzing EEG data in ambulatory settings. This study explores electrodermal activity (EDA), [...] Read more.
Emotion recognition (ER) in human–computer interaction (HCI) holds immense potential for real-world applications, but traditional approaches based on electroencephalography (EEG) face challenges due to the complexity and impracticality of collecting and analyzing EEG data in ambulatory settings. This study explores electrodermal activity (EDA), a simpler measure of the sympathetic nervous system response that can be collected at multiple peripheral body sites, as a potential alternative for ER. We investigated the variable frequency complex demodulation (VFCDM) technique to analyze EDA and EEG signals and used deep learning models (ResNet50 and MobileNetV2) to classify arousal states (high arousal, HA vs. low arousal, LA). Our results show that EDA signals analyzed by VFCDM and classified by MobileNetV2 achieve promising performance, with an accuracy of 91.45%, comparable to the best EEG-based model (91.98%), in arousal classification. This suggests that EDA offers a viable and more practically accessible approach to ER in HCI compared to traditional EEG-based methods. Future work should explore larger and more diverse datasets, incorporate valence classification through multimodal fusion, and investigate the neural mechanisms underlying EDA-EEG interactions during emotional processing to further advance robust ER for HCI applications. Full article
(This article belongs to the Special Issue Medical Physics and Physiological Measurements: Intelligent Biosensing, Wearables and AI-Driven Quantified Health)
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7 pages, 3037 KB  
Communication
Black Hole–Inspired Horizon Model for Neural Signal Dynamics
by Enrique Canessa
Biophysica 2026, 6(3), 45; https://doi.org/10.3390/biophysica6030045 - 22 May 2026
Viewed by 270
Abstract
Electroencephalographic (EEG) signals provide macroscopic observables of complex neural dynamics. We introduce a horizon-inspired framework in which measured EEG signals are modeled as projections of a complex wave-like representation constrained by an effective boundary analogous to an event horizon. In this formulation the [...] Read more.
Electroencephalographic (EEG) signals provide macroscopic observables of complex neural dynamics. We introduce a horizon-inspired framework in which measured EEG signals are modeled as projections of a complex wave-like representation constrained by an effective boundary analogous to an event horizon. In this formulation the signal amplitude obeys a renormalization-group scaling relation while EEG spectral entropy parameterizes the accessibility of observable modes. The resulting solutions generate oscillatory structures whose geometry and spectral signatures can be explored through signal analysis and sonification. This mapping between entropy-based neural observables and wave-like signal representations provides a physically motivated framework linking entropy measures, scale-dependent dynamics, and observable neural oscillations. The work is intentionally conceptual. It provides a falsifiable framework intended to stimulate future empirical investigations. Full article
(This article belongs to the Special Issue Medical Physics and Physiological Measurements: Intelligent Biosensing, Wearables and AI-Driven Quantified Health)
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