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Advances in Digital Health and Bioengineering: From Smart Sensors to AI-Driven Solutions

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 3288

Special Issue Editors

Dr. Gladiola Petroiu

E-Mail Website
Guest Editor
Faculty of Medical Bioengineering, Grigore T. Popa University of Medicine and Pharmacy of Iași, 9-13 Kogalniceanu Str., 700454 Iași, Romania
Interests: bioengineering
Special Issues, Collections and Topics in MDPI journals
Dr. Cristian Rotariu

E-Mail Website
Guest Editor
Faculty of Medical Bioengineering, Grigore T. Popa University of Medicine and Pharmacy of Iași, 9-13 Kogalniceanu Str., 700454 Iași, Romania
Interests: medical electronics; biomedical signal processing; wireless devices and sensors; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hariton-Nicolae Costin

E-Mail Website
Guest Editor
Institute of Computer Science of the Romanian Academy, Iasi Branch, 700481 Iasi, Romania
Interests: bio-signal processing; biomedical image processing; artificial intelligence (neural networks, fuzzy systems, bio-inspired algorithms); (bio)sensors/transducers; e-health and telemedicine; assistive technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of engineering and applied sciences into healthcare and bioengineering is driving major advances in the way we monitor, diagnose, and support human health. Emerging technologies such as smart sensors, wearable devices, biomedical imaging, robotics, and AI-driven data analytics are providing innovative solutions with strong practical impact, from precision diagnostics to rehabilitation and personalized medicine.

We are pleased to invite you to contribute to this Special Issue, titled “Advances in Digital Health and Bioengineering: From Smart Sensors to AI-Driven Solutions”. It aims to gather original research and reviews that highlight the latest scientific and technological progress, as well as real-world applications, in digital health and biomedical engineering.

Original research articles and reviews are welcome. Topics may include (but are not limited to) the following:

  1. Smart sensors, wearables, and IoT solutions for healthcare;
  2. AI, machine learning, and big data in medical applications;
  3. Biomedical signal and image processing for diagnostics and monitoring;
  4. Human–machine interfaces in healthcare and VR/AR technologies’
  5. Medical and rehabilitation robotics for diagnostics, therapy, and assistive care.

We look forward to receiving your contributions.

Dr. Gladiola Petroiu
Dr. Cristian Rotariu
Prof. Dr. Hariton-Nicolae Costin
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. Applied Sciences 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 2400 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

  • healthcare
  • smart sensors
  • medical robotics

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Published Papers (2 papers)

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22 pages, 2619 KB  
Article
Computational Analysis of EEG Responses to Anxiogenic Stimuli Using Machine Learning Algorithms
by Felix-Constantin Adochiei, Anamaria Ioniță, Ioana-Raluca Adochiei, Oana-Isabela Stirbu, Gladiola Petroiu and Florin Ciprian Argatu
Appl. Sci. 2026, 16(3), 1504; https://doi.org/10.3390/app16031504 - 2 Feb 2026
Viewed by 487
Abstract
Anxiety disorders are commonly assessed using instruments such as HAM-A and GAD-7. These tools rely on patient self-report and clinician interpretation, which may introduce variability. This study proposes an EEG-based computational framework for estimating anxiety levels using portable EEG recordings from the Unicorn [...] Read more.
Anxiety disorders are commonly assessed using instruments such as HAM-A and GAD-7. These tools rely on patient self-report and clinician interpretation, which may introduce variability. This study proposes an EEG-based computational framework for estimating anxiety levels using portable EEG recordings from the Unicorn Hybrid Black device. These data were harmonized with the DASPS public dataset to ensure methodological consistency. After standardized preprocessing and multi-domain feature extraction, three classifiers—logistic regression, multilayer perceptron (MLP), and k-nearest neighbors (KNN)—were trained and evaluated. Logistic regression achieved 81.25% accuracy (F1 = 0.8247), while the MLP reached 87.5% accuracy (F1 = 0.859). ROC analysis (AUC = 0.98 for logistic regression and 0.92 for MLP) confirmed that both classifiers reliably separated non-anxious from moderate participants. Severe anxiety could not be classified, reflecting the extremely limited number of participants in this category. Predicted anxiety probabilities showed significant correlations with HAM-A scores (r up to 0.71, p < 0.01), supporting the external validity of the proposed approach. Full article
(This article belongs to the Special Issue Advances in Digital Health and Bioengineering: From Smart Sensors to AI-Driven Solutions)
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29 pages, 4774 KB  
Systematic Review
Effectiveness of Digital Health Tools for Asthma Self-Management: A Systematic Review and Meta-Analysis of Clinical Trials
by Claudia Lorena Perlaza, Stephania Mina Rojas, Laura Daniela Choco, María Paula Paz González, Freiser Eceomo Cruz Mosquera and Yamil Liscano
Appl. Sci. 2025, 15(23), 12471; https://doi.org/10.3390/app152312471 - 25 Nov 2025
Viewed by 2329
Abstract
Background: Asthma is a global public health challenge, and although guidelines recommend self-management programs, their implementation is limited. Digital health tools, such as mobile applications and web platforms, have emerged as promising solutions to improve self-management, adherence, and monitoring. However, the evidence [...] Read more.
Background: Asthma is a global public health challenge, and although guidelines recommend self-management programs, their implementation is limited. Digital health tools, such as mobile applications and web platforms, have emerged as promising solutions to improve self-management, adherence, and monitoring. However, the evidence on their effectiveness is heterogeneous and often presents methodological limitations. This systematic review and meta-analysis aimed to synthesize the current evidence on the efficacy of these tools in asthma management. Methods: A systematic search was conducted in six databases for randomized controlled trials (RCTs) published between 2010 and 2025. Twenty-six RCTs that evaluated digital interventions in pediatric and adult patients with asthma were included. The outcomes of interest were asthma control, pulmonary function, symptom-free days, and health-related quality of life (HRQoL). A meta-analysis was performed using a random-effects model. Results: Digital tools showed a statistically significant improvement in pulmonary function, specifically in FEV1 (SMD: 1.53; p = 0.007) and the FEV1/FVC ratio (SMD: 1.20; p = 0.02). No significant effects were found on asthma control, PEF, symptom-free days, or HRQoL in the overall analysis. However, subgroup analyses revealed that remote supervision significantly improved asthma control, and mobile applications improved HRQoL. Conclusions: Digital health interventions are a promising complement for asthma management, notably improving pulmonary function. Their effectiveness on other clinical outcomes appears to depend on factors such as the supervision mode and the type of tool. More standardized research is needed to confirm these findings. Full article
(This article belongs to the Special Issue Advances in Digital Health and Bioengineering: From Smart Sensors to AI-Driven Solutions)
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