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Biosensors
Special Issues
Wearable Biosensing for Physiological Monitoring
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Wearable Biosensing for Physiological Monitoring

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Intelligent Biosensors and Bio-Signal Processing".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 7350

Special Issue Editors

Dr. Wanqing Wu

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Guest Editor
School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 528406, China
Interests: wearable biosensors; biosignal processing; pattern recognition; multimodal electrophysiological data fusion; AI-based health informatics; medical decision-making system; biomedical applications; smart medicine
Prof. Dr. Victor Hugo C. De Albuquerque

E-Mail Website
Guest Editor
Department of Teleinformatics Engineering (DETI), Federal University of Ceará, Fortaleza, Brazil
Interests: biomedical engineering; bioinformatics; internet of medical things; artificial intelligence; pattern recognition; signal data science; metaverse
Special Issues, Collections and Topics in MDPI journals
Dr. Simon Fong

E-Mail Website
Guest Editor
Department of Computer and Information Science, University of Macau, Room 4023, E11, FST Building, Taipa, Macau 999078, China
Interests: data stream mining; big data; advanced analytics; bio-inspired optimization algorithms and applications; business intelligence; e-commerce; biomedical applications; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
Dr. João Alexandre Lobo Marques

E-Mail Website
Guest Editor
Department of Business Administration, University of Saint Joseph, Macau 999078, China
Interests: bioengineering; digital signal processing; image processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable biosensors (WBSs) have attracted an increasing amount of interest recently, promising to be one of the greatest developments in the sector of wearable health technology.The monitoring of physiological signals has enables patient monitoring and diagnostics in clinical environments in the last few decades, and recent efforts around the miniaturization of biosensor systems and flexible electronics have increased the number of potential applications of wearable sensing, for example, in the field of remote health monitoring, rehabilitation, affective computing, etc. In addition to sensors and systems for bioelectric and vital sign monitoring such as ECG, EMG, EEG, PPG, blood pressure, and electrodermal activity, chemical and biochemical sensing solutions are also booming in wearable health monitoring and diagnostics with organic compounds and ion detection. The scope of this Special Issue is to report recent developments and advances in wearable biosensing and the analytics of their signals for general purposes or any specified application scenarios, especially for health applications and the development of wearable biosensor technology in smart medical architecture combined with new-generation information technology. 

Dr. Wanqing Wu
Dr. Victor Hugo C. de Albuquerque
Dr. Simon Fong
Dr. João Alexandre Lobo Marques
Guest Editors

If you want to learn more information or need any advice, you can contact the Special Issue Editor Jessica Zhou via <[email protected]> directly.

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. Biosensors 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

  • Application of wearable biosensor technology in smart medical architecture
  • In vitro physiological monitoring using wearable biosensors
  • Wearable biosensors to gather chemical, biological or clinically relevant information via various biomatrices
  • Artificial intelligence methods for wearable multiple modality biosignal processing
  • Machine learning techniques for wearable multiple modality biosignal processing
  • Embedded neural network and wearable biosensor system
  • E-TEXTILE-based wearable physiological monitoring
  • Wearable biosensors for medical applications
  • Biosignal processing for heterogenous wireless sensor networks
  • Multimodal biosensors data fusion

Published Papers (2 papers)

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Research

16 pages, 1890 KiB  
Article
Mental Stress Assessment Using Ultra Short Term HRV Analysis Based on Non-Linear Method
by Seungjae Lee, Ho Bin Hwang, Seongryul Park, Sanghag Kim, Jung Hee Ha, Yoojin Jang, Sejin Hwang, Hoon-Ki Park, Jongshill Lee and In Young Kim
Biosensors 2022, 12(7), 465; https://0-doi-org.brum.beds.ac.uk/10.3390/bios12070465 - 27 Jun 2022
Cited by 21 | Viewed by 3295
Abstract
Mental stress is on the rise as one of the major health problems in modern society. It is important to detect and manage mental stress to prevent various diseases caused by stress and to maintain a healthy life. The purpose of this paper [...] Read more.
Mental stress is on the rise as one of the major health problems in modern society. It is important to detect and manage mental stress to prevent various diseases caused by stress and to maintain a healthy life. The purpose of this paper is to present new heart rate variability (HRV) features based on empirical mode decomposition and to detect acute mental stress through short-term HRV (5 min) and ultra-short-term HRV (under 5 min) analysis. HRV signals were acquired from 74 young police officers using acute stressors, including the Trier Social Stress Test and horror movie viewing, and a total of 26 features, including the proposed IMF energy features and general HRV features, were extracted. A support vector machine (SVM) classification model is used to classify the stress and non-stress states through leave-one-subject-out cross-validation. The classification accuracies of short-term HRV and ultra-short-term HRV analysis are 86.5% and 90.5%, respectively. In the results of ultra-short-term HRV analysis using various time lengths, we suggest the optimal duration to detect mental stress, which can be applied to wearable devices or healthcare systems. Full article
(This article belongs to the Special Issue Wearable Biosensing for Physiological Monitoring)
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20 pages, 4427 KiB  
Article
Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics
by Yuhan Li, Ke Li, Shaofan Wang, Xiaodan Chen and Dongsheng Wen
Biosensors 2022, 12(6), 404; https://0-doi-org.brum.beds.ac.uk/10.3390/bios12060404 - 12 Jun 2022
Cited by 9 | Viewed by 2453
Abstract
With the development of the autopilot system, the main task of a pilot has changed from controlling the aircraft to supervising the autopilot system and making critical decisions. Therefore, the human–machine interaction system needs to be improved accordingly. A key step to improving [...] Read more.
With the development of the autopilot system, the main task of a pilot has changed from controlling the aircraft to supervising the autopilot system and making critical decisions. Therefore, the human–machine interaction system needs to be improved accordingly. A key step to improving the human–machine interaction system is to improve its understanding of the pilots’ status, including fatigue, stress, workload, etc. Monitoring pilots’ status can effectively prevent human error and achieve optimal human–machine collaboration. As such, there is a need to recognize pilots’ status and predict the behaviors responsible for changes of state. For this purpose, in this study, 14 Air Force cadets fly in an F-35 Lightning II Joint Strike Fighter simulator through a series of maneuvers involving takeoff, level flight, turn and hover, roll, somersault, and stall. Electro cardio (ECG), myoelectricity (EMG), galvanic skin response (GSR), respiration (RESP), and skin temperature (SKT) measurements are derived through wearable physiological data collection devices. Physiological indicators influenced by the pilot’s behavioral status are objectively analyzed. Multi-modality fusion technology (MTF) is adopted to fuse these data in the feature layer. Additionally, four classifiers are integrated to identify pilots’ behaviors in the strategy layer. The results indicate that MTF can help to recognize pilot behavior in a more comprehensive and precise way. Full article
(This article belongs to the Special Issue Wearable Biosensing for Physiological Monitoring)
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