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Long-Term Biological Signals and Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (25 October 2022) | Viewed by 9497

Special Issue Editors


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Guest Editor
Seoul National University
Interests: biological signals and systems

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Guest Editor
Kwangwoon University, 902 Saebit Bldg., 20 Gwanwoon-ro, Nowon-gu, Seoul, 01897, South Korea
Interests: physiological signal processing; machine learning

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Guest Editor
Professor of Electrical Engineering, Kookmin University, Jeongneung-Ro, Seongbuk-Gu, Seoul 02707, Korea
Interests: implantable devices; Bio-MEMS; advanced sensors for healthcare

Special Issue Information

Dear Colleagues,

Emerging wearable smart devices are applied to various vital healthcare applications such as physiological signal monitoring, therapeutic and drug delivery systems. The advances in wearable sensors have enabled long-term healthcare monitoring in home or hospital environments with recording vital physiological parameters. In particular, the long-term monitoring of the physiological parameters is a key for diagnosis of health conditions such as cardiovascular disease, diabetes, functional movement disorder, sleep deprivation, circadian rhythm disorders, etc. The long-term recording enlarges the size of the physiological dataset, and thus design and development of an appropriate data analytics on those big dataset is crucial to improve the performance of the long-term healthcare monitoring systems.

This Special Issue encourages authors, from academia and industry, to submit new research results about technological innovations and novel applications of wearable sensors and their data analytics for long-term healthcare monitoring systems. The Special Issue topics include, but are not limited to:

The topics of interest for this issue include:

  1. Wearable sensors
  2. Sensing technologies
  3. Measurement of physiological parameters
  4. Autonomous wearable sensors
  5. Textile-based wearable sensors
  6. Printed electronics
  7. Wearable IoT based systems
  8. Health care wearable sensing
  9. Circadian rhythm disorders
  10. Functional movement disorder
  11. Long-term cardiovascular disease monitoring
  12. Big data handling in physiological data analysis
  13. Artificial Intelligence algorithms
  14. Machine Learning
  15. Deep Learning

Prof. Kwang Suk Park
Prof. Cheol-Soo Park
Prof. Seung Min Lee
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. Sensors 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 2600 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.

Published Papers (2 papers)

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16 pages, 2341 KiB  
Article
Signal Quality Index Based on Template Cross-Correlation in Multimodal Biosignal Chair for Smart Healthcare
by Seunghyeok Hong, Jeong Heo and Kwang Suk Park
Sensors 2021, 21(22), 7564; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227564 - 14 Nov 2021
Cited by 5 | Viewed by 2736
Abstract
We investigated the effects of a quality screening method on unconstrained measured signals, including electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) signals, in our collective chair system for smart healthcare. Such an investigation is necessary because unattached or unbound sensors have weaker connections [...] Read more.
We investigated the effects of a quality screening method on unconstrained measured signals, including electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) signals, in our collective chair system for smart healthcare. Such an investigation is necessary because unattached or unbound sensors have weaker connections to body parts than do conventional methods. Using the biosignal chair, the physiological signals collected during sessions included a virtual driving task, a physically powered wheelchair drive, and three types of body motions. The signal quality index was defined by the similarity between the observed signals and noise-free signals from the perspective of the cross-correlations of coefficients with appropriate individual templates. The goal of the index was to qualify signals without a reference signal to assess the practical use of the chair in daily life. As expected, motion artifacts have adverse effects on the stability of physiological signals. However, we were able to observe a supplementary relationship between sensors depending on each movement trait. Except for extreme movements, the signal quality and estimated heart rate (HR) remained within the range of criteria usable for status monitoring. By investigating the signal reliability, we were able to confirm the suitability of using the unconstrained biosignal chair to collect real-life measurements to improve safety and healthcare. Full article
(This article belongs to the Special Issue Long-Term Biological Signals and Sensors)
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16 pages, 691 KiB  
Letter
Effect of an Inflatable Air Mattress with Variable Rigidity on Sleep Quality
by Hyunsoo Yu, Oh-Soon Shin, Sayup Kim and Cheolsoo Park
Sensors 2020, 20(18), 5317; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185317 - 17 Sep 2020
Cited by 3 | Viewed by 6099
Abstract
Several studies, wherein the structure or rigidity of a mattress was varied, have been conducted to improve sleep quality. These studies investigated the effect of variation in the surface characteristics of mattresses on sleep quality. The present study developed a mattress whose rigidity [...] Read more.
Several studies, wherein the structure or rigidity of a mattress was varied, have been conducted to improve sleep quality. These studies investigated the effect of variation in the surface characteristics of mattresses on sleep quality. The present study developed a mattress whose rigidity can be varied by controlling the amount of air in its air cells. To investigate the effect of the variable rigidity of the air mattress on sleep quality, participants (Male, Age: 23.9 ± 2.74, BMI: 23.3 ± 1.60) were instructed to sleep on the air mattress under different conditions, and their sleep quality was subjectively and objectively investigated. Subjectively, sleep quality is assessed based on the participants’ evaluations of the depth and length of their sleep. Objectively, sleep is estimated using the sleep stage information obtained by analysing the movements and brain waves of the participants during their sleep. A subjective assessment of the sleep quality demonstrates that the participants’ sleep was worse with the adjustment of the air mattress than that without; however, the objective sleep quality results demonstrates an improvement in the sleep quality when the rigidity of the air mattress is varied based on the participant’s preference. This paper proposes a design for mattresses that can result in more efficient sleep than that provided by traditional mattresses. Full article
(This article belongs to the Special Issue Long-Term Biological Signals and Sensors)
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