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Electronics
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Internet of Things for E-health
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Internet of Things for E-health

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 November 2024) | Viewed by 7591

Special Issue Editor

Dr. Yiannis Koumpouros

E-Mail Website
Guest Editor
Digital Innovation in Public Health Research Lab—DigInHealth, Department of Public and Community Health, University of West Attica, 11521 Athens, Greece
Interests: rehabilitation; health informatics; m-health; e-health; telemedicine; assistive technologies; users satisfaction assessment; strategic management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Integration of the Internet of Things (IoT) in the field of e-health has brought about transformative advancements in healthcare; the convergence of the IoT and e-health has opened up new possibilities for remote patient monitoring, personalized healthcare, and efficient medical interventions. By connecting medical devices, wearables, and sensors to the internet, the IoT enables seamless communication and data exchange between patients, healthcare providers, and medical systems. This connectivity allows for remote patient monitoring, real-time health data collection, and analysis, leading to more efficient and personalized healthcare services. This continuous stream of information allows for proactive healthcare management, early detection of health issues, and timely interventions. IoT-enabled devices can transmit data securely to healthcare professionals, enabling remote monitoring and reducing the need for frequent hospital visits. The IoT in e-health facilitates proactive healthcare management by continuously monitoring patients' vital signs, medication adherence, and other relevant parameters. With remote monitoring capabilities, healthcare professionals can promptly detect any abnormalities or changes in a patient's health status and intervene accordingly, even from a distance. Furthermore, the IoT in e-health can enhance patient engagement and empowerment by providing them with real-time feedback, personalized recommendations, and the ability to monitor their progress. The ability to analyze vast amounts of data generated by IoT devices also facilitates predictive analytics and personalized treatment plans. As the Internet of Things continues to advance, its integration with e-health promises to enhance healthcare accessibility, improve patient outcomes, and transform the way healthcare services are delivered and experienced.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Remote Patient Monitoring: Exploring the role of the IoT in enabling the remote monitoring of patients' health conditions, vital signs, and medication adherence.
  • Wearable Devices and IoT in mHealth: Investigating the role of wearable devices, such as smartwatches, fitness trackers, and biosensors, in conjunction with the IoT for monitoring health metrics, activity tracking, and delivering personalized health interventions.
  • IoT-enabled Telemedicine: Investigating how IoT technologies enhance telemedicine by facilitating virtual consultations, remote diagnostics, and the telemonitoring of patients.
  • Data Security and Privacy in IoT-driven E-Health: Analyzing the challenges and best practices for ensuring data security and protecting patient privacy in IoT-based e-health systems.
  • Smart Hospitals: Exploring the implementation of IoT technologies in hospital settings to improve operational efficiency, asset tracking, patient flow management, and patient safety.
  • IoT-enabled Medication Management: Investigating the use of IoT devices and systems to enhance medication management, including smart pill dispensers, medication adherence monitoring, and remote medication tracking.
  • IoT and Chronic Disease Management: Examining how IoT technologies can assist in managing chronic diseases, such as diabetes, asthma, and hypertension, through remote monitoring, personalized treatment plans, and behavior tracking.
  • IoT in Emergency Medical Services: Exploring the role of the IoT in emergency medical services, including the real-time tracking of ambulances, remote patient monitoring during transportation, and IoT-enabled emergency response systems.
  • Healthcare Data Analytics with IoT: Discussing the application of IoT-generated healthcare data in data analytics and predictive modeling to improve healthcare decision making and outcomes.
  • Ethical and Legal Considerations in IoT-driven E-Health: Examining the ethical and legal implications of using IoT technologies in e-health, including patient consent, data ownership, and the potential risks associated with IoT devices and systems.

Dr. Yiannis Koumpouros
Guest Editor

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. Electronics 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

  • IoT
  • e-health
  • mhealth
  • telehealth
  • sensors
  • healthcare

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Published Papers (4 papers)

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Research

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17 pages, 1425 KiB  
Article
Sudden Cardiac Death Risk Prediction Based on Noise Interfered Single-Lead ECG Signals
by Weidong Gao and Jie Liao
Electronics 2024, 13(21), 4274; https://doi.org/10.3390/electronics13214274 - 31 Oct 2024
Viewed by 2043
Abstract
Sudden cardiac death (SCD) represents a critical acute cardiovascular event characterized by rapid onset of cardiac and respiratory arrest, posing a significant threat to patients due to its high fatality rate. Monitoring indices related to SCD using wearable devices holds profound implications for [...] Read more.
Sudden cardiac death (SCD) represents a critical acute cardiovascular event characterized by rapid onset of cardiac and respiratory arrest, posing a significant threat to patients due to its high fatality rate. Monitoring indices related to SCD using wearable devices holds profound implications for preemptive measures aimed at reducing the incidence of such life-threatening events. Hence, this study proposed a predictive algorithm for SCD leveraging single-lead electrocardiogram (ECG) signals featuring low signal-to-noise ratios. Initially, simulated electrode motion artifact noise was introduced to ideal ECG signals to emulate the signal conditions with low signal-to-noise ratios encountered in everyday scenarios. To meet the criteria of simplicity and cost-effectiveness required for wearable devices, the analysis focused exclusively on single-lead signals. The proposed algorithm in this study employed a lightweight machine learning approach to extract 12-dimensional features encompassing ventricular late potentials, T-wave electrical alternation, and corrected QT intervals from the signal. The algorithm achieved an average prediction accuracy of 93.22% within 30 min prior to SCD onset, and 95.43% when utilizing a normal sinus rhythm database as a control, demonstrating robust performance. Additionally, a comprehensive Sudden Cardiac Death Index (SCDI) was devised to quantify the risk of SCD, formulated by integrating pivotal two-dimensional features contributing significantly to the algorithm. This index effectively distinguishes high-risk signals indicative of SCD from normal signals, thereby offering valuable supplementary insights in clinical settings. Full article
(This article belongs to the Special Issue Internet of Things for E-health)
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16 pages, 3710 KiB  
Article
A Multi-Modal IoT Framework for Healthy Nutritional Choices in Everyday Childhood Life
by Georgios Bardis, Yiannis Koumpouros, Nikolaos Sideris and Christos Troussas
Electronics 2024, 13(11), 2086; https://doi.org/10.3390/electronics13112086 - 27 May 2024
Viewed by 921
Abstract
Policies, standards and recommendations for healthy childhood nutritional choices are well-defined and widely available, yet a significant percentage of children as well as parents and caregivers, fail to become aware and follow them despite the intense technological penetration and information abundance in everyday [...] Read more.
Policies, standards and recommendations for healthy childhood nutritional choices are well-defined and widely available, yet a significant percentage of children as well as parents and caregivers, fail to become aware and follow them despite the intense technological penetration and information abundance in everyday life. The aim of this work was to establish an IoT-integrated framework parlaying the current technological platforms’ capabilities to streamline the aforementioned policies, standards, and recommendations in a transparent, highly adoptable, and attractive scheme for children while being minimally demanding for responsible adults through a set of readily available innovative services and smart devices. The rationale was to obtain information concerning nutritional choices and habits with minimum intervention through smart devices, minimizing user deviation from everyday routines in order to consolidate, visualize, and exploit this information in an engaging and motivating gaming environment, maximizing visual impact while maintaining a minimal computational footprint. Full article
(This article belongs to the Special Issue Internet of Things for E-health)
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12 pages, 4297 KiB  
Article
Wearable IoT System for Hand Function Assessment Based on EMG Signals
by Zhenhao Zhi and Qun Wu
Electronics 2024, 13(4), 778; https://doi.org/10.3390/electronics13040778 - 16 Feb 2024
Cited by 2 | Viewed by 1843
Abstract
Evaluating hand function presents a significant challenge in the realm of remote rehabilitation, particularly when highlighting the need for comfort and practicality in wearable devices. This research introduces an innovative wearable device-based Internet of Things (IoT) system, specifically designed for the assessment of [...] Read more.
Evaluating hand function presents a significant challenge in the realm of remote rehabilitation, particularly when highlighting the need for comfort and practicality in wearable devices. This research introduces an innovative wearable device-based Internet of Things (IoT) system, specifically designed for the assessment of hand function, with a focus on a wearable wristband. The system, enhanced by cloud technology, offers comprehensive solutions for remote health management and therapeutic services. Firstly, it uses electromyography (EMG) signals from the arm to assess hand function. By employing sophisticated classification and regression models, this system can automatically identify user gestures and accurately measure grip strength. Additionally, the integration of additional sensor data ensures that the system fulfills essential criteria for hand function assessment. Leaving conventional grip strength classification methods, this study explored four distinct regression models to accurately represent the grip strength curve. The findings reveal that the Random Forest Regression (RFR) model is the most effective, achieving an R2 score of 0.9563 on the test data. This significant outcome not only confirms the practicality of the wearable wristband, which relies on EMG signals, but also underscores the potential of the IoT system in assessing hand function. Full article
(This article belongs to the Special Issue Internet of Things for E-health)
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Review

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24 pages, 5414 KiB  
Review
Internet of Things Ontologies for Well-Being, Aging and Health: A Scoping Literature Review
by Hrvoje Belani, Petar Šolić, Eftim Zdravevski and Vladimir Trajkovik
Electronics 2025, 14(2), 394; https://doi.org/10.3390/electronics14020394 - 20 Jan 2025
Viewed by 1121
Abstract
Internet of Things aims to simplify and automate complicated tasks by using sensors and other inputs for collecting huge amounts of data, processing them in the cloud and on the edge networks, and allowing decision making toward further interactions via actuators and other [...] Read more.
Internet of Things aims to simplify and automate complicated tasks by using sensors and other inputs for collecting huge amounts of data, processing them in the cloud and on the edge networks, and allowing decision making toward further interactions via actuators and other outputs. As connected IoT devices rank in billions, semantic interoperability remains one of the permanent challenges, where ontologies can provide a great contribution. The main goal of this paper is to analyze the state of research on semantic interoperability in well-being, aging, and health IoT services by using ontologies. This was achieved by analyzing the following research questions: “Which IoT ontologies have been used to implement well-being, aging and health services?” and “What is the dominant approach to achieve semantic interoperability of IoT solutions for well-being, aging and health?’ We conducted a scoping literature review of research papers from 2013 to 2024 by applying the PRISMA-ScR meta-analysis methodology with a custom-built software tool for an exhaustive search through the following digital libraries: IEEE Xplore, PubMed, MDPI, Elsevier ScienceDirect, and Springer Nature Link. By thoroughly analyzing 30 studies from an initial pool of more than 80,000 studies, we conclude that IoT ontologies for well-being, aging, and health services increasingly adopt Semantic Web of Things standards to achieve semantic interoperability by integrating heterogeneous data through unified semantic models. Emerging approaches, like semantic communication, Large Language Models Edge Intelligence, and sustainability-driven IoT analytics, can further enhance service efficiency and promote a holistic “One Well-Being, Aging, and Health” framework. Full article
(This article belongs to the Special Issue Internet of Things for E-health)
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