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Bioengineering
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IoT Technology in Bioengineering Applications: Second Edition
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IoT Technology in Bioengineering Applications: Second Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 7959

Special Issue Editors

Dr. Mihaela Hnatiuc

E-Mail Website
Guest Editor
Electronic and Telecommunication Department, Constanta Maritime University, 104 Mircea cel Batran, 900663 Constanta, Romania
Interests: electronic embedded systems; intelligent sensors and interface; smart home; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Larbi Boubchir

E-Mail Website
Guest Editor
LIASD Research Lab, Department of Computer Science, University of Paris 8, 93526 Saint-Denis, France
Interests: biomedical signal processing; EEG; image processing; machine learning; brain–computer interface; biometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is the second volume of the previous Special Issue on "IoT Technology in Bioengineering Applications".

This Special Issue presents novel solutions to challenging real-world problems applying IoT devices to bioengineering. IoT technology is used in therapies, implants, diagnostics, adaptive prosthetics, etc., where data are recorded and processed in the cloud for Internet-based uses. This method was developed for remote monitoring to improve people's lives. At the same time, eco plants and biofoods greatly impact human health. IoT technology is used to monitor and diagnose farms and food to improve the nutrient and food quality.

The "IoT Technology in Bioengineering Applications: Second Edition" issue publishes research using quantitative tools, including simulation and mathematical modeling. This Special Issue focuses on the exciting applications of bioengineering science in health, medicine, and agronomy.

Dr. Mihaela Hnatiuc
Prof. Dr. Larbi Boubchir
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. Bioengineering 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 2700 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

  • robotic device
  • signal processing
  • image processing
  • communication protocol
  • embedded system
  • smart sensors
  • cloud/FOG
  • predictive methods
  • monitoring
  • process optimization
  • diagnosis
  • implant
  • tele surgery
  • teleconsultation
  • telemonitoring

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Related Special Issue

Published Papers (4 papers)

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Research

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21 pages, 6504 KB  
Article
Detection of Sleep Posture via Humidity Fluctuation Analysis in a Sensor-Embedded Pillow
by Won-Ho Jun and Youn-Sik Hong
Bioengineering 2025, 12(5), 480; https://doi.org/10.3390/bioengineering12050480 - 30 Apr 2025
Viewed by 2420
Abstract
This study presents a novel method for detecting sleep posture changes—specifically tossing and turning—by monitoring variations in humidity using an array of humidity sensors embedded at regular intervals within a memory-foam pillow. Unlike previous approaches that rely primarily on temperature or pressure sensors, [...] Read more.
This study presents a novel method for detecting sleep posture changes—specifically tossing and turning—by monitoring variations in humidity using an array of humidity sensors embedded at regular intervals within a memory-foam pillow. Unlike previous approaches that rely primarily on temperature or pressure sensors, our method leverages the observation that humidity fluctuations are more pronounced during movement, enabling the more sensitive detection of posture changes. We demonstrate that dynamic patterns in humidity data correlate strongly with physical motion during sleep. To identify these transitions, we applied the Pruned Exact Linear Time (PELT) algorithm, which effectively segmented the time series based on abrupt changes in humidity. Furthermore, we converted humidity fluctuation curves into image representations and employed a transfer-learning-based model to classify sleep postures, achieving accurate recognition performance. Our findings highlight the potential of humidity sensing as a reliable modality for non-invasive sleep monitoring. In this study, we propose a novel method for detecting tossing and turning during sleep by analyzing changes in humidity captured by a linear array of sensors embedded in a memory foam pillow. Compared to temperature data, humidity data exhibited more significant fluctuations, which were leveraged to track head movement and infer sleep posture. We applied a rolling smoothing technique and quantified the cumulative deviation across sensors to identify posture transitions. Furthermore, the PELT algorithm was utilized for precise change-point detection. To classify sleep posture, we converted the humidity time series into images and implemented a transfer learning model using a Vision Transformer, achieving a classification accuracy of approximately 96%. Our results demonstrate the feasibility of a sleep posture analysis using only humidity data, offering a non-intrusive and effective approach for sleep monitoring. Full article
(This article belongs to the Special Issue IoT Technology in Bioengineering Applications: Second Edition)
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Review

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33 pages, 1529 KB  
Review
Smart Devices and Multimodal Systems for Mental Health Monitoring: From Theory to Application
by Andreea Violeta Caragață, Mihaela Hnatiuc, Oana Geman, Simona Halunga, Adrian Tulbure and Catalin J. Iov
Bioengineering 2026, 13(2), 165; https://doi.org/10.3390/bioengineering13020165 - 29 Jan 2026
Cited by 1 | Viewed by 2027
Abstract
Smart devices and multimodal biosignal systems, including electroencephalography (EEG/MEG), ECG-derived heart rate variability (HRV), and electromyography (EMG), increasingly supported by artificial intelligence (AI), are being explored to improve the assessment and longitudinal monitoring of mental health conditions. Despite rapid growth, the available evidence [...] Read more.
Smart devices and multimodal biosignal systems, including electroencephalography (EEG/MEG), ECG-derived heart rate variability (HRV), and electromyography (EMG), increasingly supported by artificial intelligence (AI), are being explored to improve the assessment and longitudinal monitoring of mental health conditions. Despite rapid growth, the available evidence remains heterogeneous, and clinical translation is limited by variability in acquisition protocols, analytical pipelines, and validation quality. This systematic review synthesizes current applications, signal-processing approaches, and methodological limitations of biosignal-based smart systems for mental health monitoring. Methods: A PRISMA 2020-guided systematic review was conducted across PubMed/MEDLINE, Scopus, the Web of Science Core Collection, IEEE Xplore, and the ACM Digital Library for studies published between 2013 and 2026. Eligible records reported human applications of wearable/smart devices or multimodal biosignals (e.g., EEG/MEG, ECG/HRV, EMG, EDA/GSR, and sleep/activity) for the detection, monitoring, or management of mental health outcomes. The reviewed literature after predefined inclusion/exclusion criteria clustered into six themes: depression detection and monitoring (37%), stress/anxiety management (18%), post-traumatic stress disorder (PTSD)/trauma (5%), technological innovations for monitoring (25%), brain-state-dependent stimulation/interventions (3%), and socioeconomic context (7%). Across modalities, common analytical pipelines included artifact suppression, feature extraction (time/frequency/nonlinear indices such as entropy and complexity), and machine learning/deep learning models (e.g., SVM, random forests, CNNs, and transformers) for classification or prediction. However, 67% of studies involved sample sizes below 100 participants, limited ecological validity, and lacked external validation; heterogeneity in protocols and outcomes constrained comparability. Conclusions: Overall, multimodal systems demonstrate strong potential to augment conventional mental health assessment, particularly via wearable cardiac metrics and passive sensing approaches, but current evidence is dominated by proof-of-concept studies. Future work should prioritize standardized reporting, rigorous validation in diverse real-world cohorts, transparent model evaluations, and ethics-by-design principles (privacy, fairness, and clinical governance) to support translation into practice. Full article
(This article belongs to the Special Issue IoT Technology in Bioengineering Applications: Second Edition)
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Other

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22 pages, 1330 KB  
Systematic Review
Effects of Exercise-Based Telerehabilitation for Knee Osteoarthritis: A Systematic Review and a Study Protocol
by Giacomo Farì, Francesco Quarta, Federica Bressi, Raffaele La Russa, Teresa Paolucci and Andrea Bernetti
Bioengineering 2026, 13(2), 136; https://doi.org/10.3390/bioengineering13020136 - 24 Jan 2026
Viewed by 1042
Abstract
Background: Knee osteoarthritis causes considerable pain and disability. Telerehabilitation has emerged as a promising treatment option, especially after the Coronavirus Disease 2019 pandemic, but it still faces challenges regarding solid scientific evidence about its multiple benefits. This systematic review aimed to analyze the [...] Read more.
Background: Knee osteoarthritis causes considerable pain and disability. Telerehabilitation has emerged as a promising treatment option, especially after the Coronavirus Disease 2019 pandemic, but it still faces challenges regarding solid scientific evidence about its multiple benefits. This systematic review aimed to analyze the reported beneficial effects of telerehabilitation based on therapeutic exercise for the management of knee osteoarthritis. Methodsː PubMed, PEDro, Web of Science and Cochrane Library databases were used to identify eligible studies. This review followed the PRISMA guidelines and was registered at PROSPERO (n° CRD42024579836). The selected studies underwent a qualitative assessment using the Modified Jadad Score. Results: Ten studies, including a total of 1354 participants, were included. From the selected studies, a wide variety of outcome measures emerged to evaluate the efficacy of telerehabilitation in the relief of pain and its clinical consequences. Seven studies specifically assessed pain, with four showing significant improvements in pain reduction in the intervention group compared with the control group. Telerehabilitation was found to be more effective or non-inferior to traditional rehabilitation in relieving pain, as reported across various pain scales. Limitations include the heterogeneity of interventions, the exclusion of non-recent studies, and the exclusive focus on therapeutic exercise. Conclusionsː The results of this systematic review suggest that telerehabilitation provides pain relief, improves physical function, and enhances quality of life, while preliminary evidence indicates potential cost-related advantages. However, some studies did not find TR to be superior to control interventions, highlighting mixed evidence. Additional high-quality studies are required to better support this promising rehabilitation approach. Full article
(This article belongs to the Special Issue IoT Technology in Bioengineering Applications: Second Edition)
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25 pages, 2256 KB  
Perspective
Immersive Virtual Reality Environments as Psychoanalytic Settings: A Conceptual Framework for Modeling Unconscious Processes Through IoT-Based Bioengineering Interfaces
by Vincenzo Maria Romeo
Bioengineering 2025, 12(11), 1257; https://doi.org/10.3390/bioengineering12111257 - 17 Nov 2025
Viewed by 1561
Abstract
Background: Immersive Virtual Reality (IVR) is gaining increasing relevance in the field of mental health as a tool for therapeutic simulation and embodied experience. However, most existing VR applications are grounded in cognitive–behavioral frameworks, leaving unexplored the integration of symbolic, intersubjective, and unconscious [...] Read more.
Background: Immersive Virtual Reality (IVR) is gaining increasing relevance in the field of mental health as a tool for therapeutic simulation and embodied experience. However, most existing VR applications are grounded in cognitive–behavioral frameworks, leaving unexplored the integration of symbolic, intersubjective, and unconscious dimensions. Psychoanalysis—particularly its constructs of setting, rêverie, and transference—offers a unique epistemological basis for designing therapeutic environments that engage implicit emotional processes. Aim: This paper aims to develop a conceptual framework for modeling IVR-based therapeutic settings inspired by psychoanalytic theory and enhanced through IoT-enabled biosensing technologies. Methods/Approach: We propose a three-layer architecture: (1) a somatic layer involving IoT-based real-time physiological monitoring (e.g., heart rate variability, galvanic skin response, eye-tracking, EEG); (2) a symbolic-narrative layer where the VR environment dynamically adapts to the user’s affective state through immersive visual and auditory stimuli; and (3) a relational layer where AI-driven avatars simulate transferential dynamics. The model is theoretically grounded in psychoanalytic literature and informed by current advances in affective computing and bioengineering. Conclusions: By bridging psychoanalytic metapsychology and bioengineering design, this framework proposes a novel approach to therapeutic IVR systems that move beyond explicit cognition to engage the embodied unconscious. The integration of IoT biosignals enables the mapping and modulation of internal states within a structured symbolic space, opening new pathways for the clinical application of digital psychoanalysis. Full article
(This article belongs to the Special Issue IoT Technology in Bioengineering Applications: Second Edition)
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