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Applied Sciences
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Advanced Biosensor, Signal Processing and Computers Sciences in Medicine and...
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Advanced Biosensor, Signal Processing and Computers Sciences in Medicine and (Bio)medical Engineering

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

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 3136

Special Issue Editors

Dr. Réda Yahiaoui

E-Mail Website
Guest Editor
Sciences and Technical UFR Electronic Department Laboratoire de Nanomedicine, Imagerie, Thérapeutique, EA4662 University of Franche Comté, 25030 Besançon, France
Interests: embedded systems; bio micro and nano-devices; MEMS characterization; microfluidics; biomedical instrumentation; measurement; control systems; remote monitoring; IoT and IA
Special Issues, Collections and Topics in MDPI journals
Dr. Mahmoud Addouche

E-Mail Website
Guest Editor
MN2S Department, Phononique & Microscopies Team, UFC - UFR ST, 25030 Besançon, France
Interests: metamaterials; acoustics; optomechanics

Special Issue Information

Dear Colleagues,

The last global COVID-19 pandemic was a tough test for the global health system. Most local health systems have proven to need to prepare to deal with a new outbreak. In addition, most deaths related to the disease concern vulnerable populations (elderly people with chronic diseases). The unprecedented nature of the global health context has posed many scientific challenges for health personnel and research teams.

Therefore, this interdisciplinary Special Issue is intended for researchers, engineers, and healthcare professionals to share their latest research results and developments in biosensors, signal processing, and computer sciences in medicine and (bio)medical engineering.

This Special Issue will cover the following areas of interest:

  1. Advanced biosensors (portable sensors, implementable sensors, non-invasive sensors, external sensors, and visual sensors).
  2. Signal and information processing techniques based on artificial intelligence, machine learning, and deep learning.

Overall, this Special Issue will publish original research highlighting the applications of biosensors, signal processing, and computer science to revolutionize telemonitoring in healthcare by improving medical diagnosis, therapy, remote monitoring of vulnerable people, and disease management.

Dr. Réda Yahiaoui
Dr. Mahmoud Addouche
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. 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

  • biosensors
  • signal processing
  • computer science
  • medicine
  • telemonitoring
  • vulnerable people
  • bio(medical) engineering

Published Papers (2 papers)

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Review

36 pages, 610 KiB  
Review
Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
by Moustafa Fayad, Mohamed-Yacine Hachani, Kamal Ghoumid, Ahmed Mostefaoui, Samir Chouali, Fabien Picaud, Guillaume Herlem, Isabelle Lajoie and Réda Yahiaoui
Appl. Sci. 2023, 13(18), 10352; https://0-doi-org.brum.beds.ac.uk/10.3390/app131810352 - 15 Sep 2023
Cited by 1 | Viewed by 1446
Abstract
The severity of falls increases with age and reduced mobility. Falls are a frequent source of domestic accidents and accidental death on the part of fragile people. They produce anatomical injuries, reduce quality of life, cause dramatic psychological effects, and impose heavy financial [...] Read more.
The severity of falls increases with age and reduced mobility. Falls are a frequent source of domestic accidents and accidental death on the part of fragile people. They produce anatomical injuries, reduce quality of life, cause dramatic psychological effects, and impose heavy financial burdens. A growing elderly population leads to a direct increase in health service costs, and indirectly to a deterioration of social life in the long term. Unsurprisingly, socioeconomic costs have triggered new scientific health research to detect falls in older people. One of the most appropriate solutions for monitoring the elderly and automatically detecting falls is computer vision. The Kinect camera plays a vital role in recognizing and detecting activities while ensuring seniors’ comfort, safety, and privacy preferences in the fall detection system. This research surveys several Kinect-based works in the literature that cover the approaches used in fall detection. In addition, we discuss the public fall benchmark based on Kinect technology. In general, the main objective of this survey is to provide a complete description of the modules making up the fall detectors and thereby guide researchers in developing fall approaches based on Kinect. Full article
(This article belongs to the Special Issue Advanced Biosensor, Signal Processing and Computers Sciences in Medicine and (Bio)medical Engineering)
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30 pages, 1714 KiB  
Review
Exploring the Potential of Sensing for Breast Cancer Detection
by Nure Alam Chowdhury, Lulu Wang, Linxia Gu and Mehmet Kaya
Appl. Sci. 2023, 13(17), 9982; https://0-doi-org.brum.beds.ac.uk/10.3390/app13179982 - 04 Sep 2023
Cited by 2 | Viewed by 1304
Abstract
Breast cancer is a generalized global problem. Biomarkers are the active substances that have been considered as the signature of the existence and evolution of cancer. Early screening of different biomarkers associated with breast cancer can help doctors to design a treatment plan. [...] Read more.
Breast cancer is a generalized global problem. Biomarkers are the active substances that have been considered as the signature of the existence and evolution of cancer. Early screening of different biomarkers associated with breast cancer can help doctors to design a treatment plan. However, each screening technique for breast cancer has some limitations. In most cases, a single technique can detect a single biomarker at a specific time. In this study, we address different types of biomarkers associated with breast cancer. This review article presents a detailed picture of different techniques and each technique’s associated mechanism, sensitivity, limit of detection, and linear range for breast cancer detection at early stages. The limitations of existing approaches require researchers to modify and develop new methods to identify cancer biomarkers at early stages. Full article
(This article belongs to the Special Issue Advanced Biosensor, Signal Processing and Computers Sciences in Medicine and (Bio)medical Engineering)
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