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Bioengineering
Special Issues
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: 31 August 2025 | Viewed by 344

Special Issue Editors

Dr. Mihaela Hnatiuc

E-Mail Website
Guest Editor
Electronic and Telecommunication Departament, 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 100 words) can be sent to the Editorial Office for announcement on this website.

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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Published Papers (1 paper)

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21 pages, 6504 KiB  
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 (registering DOI) - 30 Apr 2025
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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