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Innovative Sensors and IoT for AI-Enabled Smart Healthcare

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

Deadline for manuscript submissions: 20 October 2024 | Viewed by 1773

Special Issue Editors


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Guest Editor
Department of Computer Science and Engineering, School of Engineering, American University of Ras al Khaimah, Ras al Khaimah P.O. Box 10021, United Arab Emirates
Interests: neural engineering; low power sensors/IoT devices; hardware accelerators; FPGAs/ASIC; applied artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The intersection of the innovative sensors, Internet of Things (IoT) and healthcare has ushered in a new era of medical advancements, revolutionizing patient care, data-driven diagnosis, and health monitoring. This special issue aims to explore the latest developments and innovations in the field of IoT and Smart Healthcare, focusing on cutting-edge research and technologies that enable the seamless integration of IoT devices into the healthcare ecosystem. As we move into a more connected and data-centric world, the importance of efficient and secure healthcare solutions cannot be overstated. This special issue will provide a platform for researchers, engineers, and practitioners to share their expertise and insights in this rapidly evolving domain.

In recent years, the healthcare industry has seen a dramatic transformation with the adoption of IoT technologies. The COVID-19 pandemic accelerated the need for remote healthcare monitoring and telemedicine, underlining the urgency of this special issue. Furthermore, advancements in Artificial Intelligence (AI), hardware accelerators, bendable electronics, and reconfigurable low-power IoT devices are crucial components of smart healthcare systems. These technologies enable real-time data analysis, personalized treatment, and efficient resource management, ultimately improving patient outcomes and reducing healthcare costs.

Dr. Chan Hwang See
Dr. Arfan Ghani
Prof. Dr. Simeon Keates
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.

Keywords

  • machine learning and deep learning applications in disease detection, prediction, and personalized treatment
  • RFID Sensors for the IoT healthcare system
  • neuromorphic healthcare sensors Thin film, bendable electronics for healthcare monitoring FPGA and GPU-based accelerators for real-time data processing in medical devices
  • sensors and devices for continuous vital sign monitoring and health assessment
  • smart drug administration and dosage control
  • data encryption, authentication, and privacy in healthcare IoT
  • telehealth solutions for remote patient care and disease management
  • big data analytics and data-driven decision-making in healthcare
  • IoT integration in medical imaging modalities for improved diagnostics
  • building scalable and resilient IoT architectures for healthcare applications
  • user-friendly interfaces for patients and healthcare professionals
  • addressing the ethical and regulatory challenges of IoT in healthcare

 

Published Papers (2 papers)

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Research

12 pages, 3876 KiB  
Article
SPR and Double Resonance LPG Biosensors for Helicobacter pylori BabA Antigen Detection
by Georgi Dyankov, Tinko Eftimov, Evdokiya Hikova, Hristo Najdenski, Vesselin Kussovski, Petia Genova-Kalou, Vihar Mankov, Hristo Kisov, Petar Veselinov, Sanaz Shoar Ghaffari, Mila Kovacheva-Slavova, Borislav Vladimirov and Nikola Malinowski
Sensors 2024, 24(7), 2118; https://doi.org/10.3390/s24072118 - 26 Mar 2024
Viewed by 605
Abstract
Given the medical and social significance of Helicobacter pylori infection, timely and reliable diagnosis of the disease is required. The traditional invasive and non-invasive conventional diagnostic techniques have several limitations. Recently, opportunities for new diagnostic methods have appeared based on the recent advance [...] Read more.
Given the medical and social significance of Helicobacter pylori infection, timely and reliable diagnosis of the disease is required. The traditional invasive and non-invasive conventional diagnostic techniques have several limitations. Recently, opportunities for new diagnostic methods have appeared based on the recent advance in the study of H. pylori outer membrane proteins and their identified receptors. In the present study we assess the way in which outer membrane protein–cell receptor reactions are applicable in establishing a reliable diagnosis. Herein, as well as in other previous studies of ours, we explore the reliability of the binding reaction between the best characterized H. pylori adhesin BabA and its receptor, the blood antigen Leb. For the purpose we developed surface plasmon resonance (SPR) and double resonance long period grating (DR LPG) biosensors based on the BabA–Leb binding reaction for diagnosing H. pylori infection. In SPR detection, the sensitivity was estimated at 3000 CFU/mL—a much higher sensitivity than that of the RUT test. The DR LPG biosensor proved to be superior in terms of accuracy and sensitivity—concentrations as low as 102 CFU/mL were detected. Full article
(This article belongs to the Special Issue Innovative Sensors and IoT for AI-Enabled Smart Healthcare)
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18 pages, 2728 KiB  
Article
Machine Learning Method and Hyperspectral Imaging for Precise Determination of Glucose and Silicon Levels
by Adam Wawerski, Barbara Siemiątkowska, Michał Józwik, Bartłomiej Fajdek and Małgorzata Partyka
Sensors 2024, 24(4), 1306; https://doi.org/10.3390/s24041306 - 18 Feb 2024
Cited by 1 | Viewed by 806
Abstract
This article introduces an algorithm for detecting glucose and silicon levels in solution. The research focuses on addressing the critical need for accurate and efficient glucose monitoring, particularly in the context of diabetic management. Understanding and monitoring silicon levels in the body is [...] Read more.
This article introduces an algorithm for detecting glucose and silicon levels in solution. The research focuses on addressing the critical need for accurate and efficient glucose monitoring, particularly in the context of diabetic management. Understanding and monitoring silicon levels in the body is crucial due to its significant role in various physiological processes. Silicon, while often overshadowed by other minerals, plays a vital role in bone health, collagen formation, and connective tissue integrity. Moreover, recent research suggests its potential involvement in neurological health and the prevention of certain degenerative diseases. Investigating silicon levels becomes essential for a comprehensive understanding of its impact on overall health and well-being and paves the way for targeted interventions and personalized healthcare strategies. The approach presented in this paper is based on the integration of hyperspectral data and artificial intelligence techniques. The algorithm investigates the effectiveness of two distinct models utilizing SVMR and a perceptron independently. SVMR is employed to establish a robust regression model that maps input features to continuous glucose and silicon values. The study outlines the methodology, including feature selection, model training, and evaluation metrics. Experimental results demonstrate the algorithm’s effectiveness at accurately predicting glucose and silicon concentrations and showcases its potential for real-world application in continuous glucose and silicon monitoring systems. Full article
(This article belongs to the Special Issue Innovative Sensors and IoT for AI-Enabled Smart Healthcare)
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