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IoT for Healthcare and Wellbeing: Trends, Challenges, and Applications, 2nd...
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IoT for Healthcare and Wellbeing: Trends, Challenges, and Applications, 2nd Edition

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 14733

Special Issue Editors

Dr. Francisco Luna-Perejón

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Guest Editor
Department of Architecture and Computer Technology, University of Seville, Seville, Spain
Interests: artificial neural networks; machine learning; deep learning; e-health; embedded systems; biomedical instrumentation
Special Issues, Collections and Topics in MDPI journals
Dr. Lourdes Miró Amarante

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Department of Architecture and Computer Technology, University of Seville, Seville, Spain
Interests: embedded systems; biomedical instrumentation; e-health; sensors; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Francisco Gómez-Rodríguez

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Guest Editor
Department of Architecture and Computer Technology, University of Seville, Seville, Spain
Interests: embedded systems; biomedical instrumentation; sensors; IoT; neuromorphic engineering; artificial vision; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Javier Civit Masot

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Department of Architecture and Computer Technology, University of Seville, Seville, Spain
Interests: biomedical engineering; deep learning; medical imaging; medical instrumentation
Dr. Luis Muñoz

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Guest Editor
Department of Architecture and Computer Technology, University of Seville, Seville, Spain
Interests: HPC; deep learning; Machine Learning；Emotion recognition; Virtual Reality

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) systems are covering a growing number of application areas that intervene in our daily lives. Applied to health and wellness care, this technology provides continuous and ubiquitous monitoring and assistance, allowing the creation of valuable tools for diagnosis, health empowerment, and personalized treatment, among others.

As new systems and applications are created, challenges arise when applying this kind of technology into health areas. These challenges include the management of personal data sensed by devices, accurate acquisition of biomedical signals, integration with electronic health records, adapting devices to be comfortable to wear by the user, and optimizing energy consumption.

For this Special Issue, our aim is to present novel approaches to IoT systems for health and wellness care. Reviews and survey papers are also welcomed. Topics of interest include but are not limited to:

- Novel IoT devices and systems for wellbeing and healthcare;
- Protocols and standards for transmission and integration with electronic health records;
- Privacy and security in the communication of biomedical data;
- Usability and user experience in wellbeing and healthcare IoT systems;
- Application of AI technologies for monitoring, diagnosis, and prevention;
- Explainable AI applied to IoT systems for diagnosis support;
- Context-aware and adaptive IoT systems for health and wellness care personalization.

Dr. Francisco Luna-Perejón
Dr. Lourdes Miró Amarante
Dr. Francisco Gómez-Rodríguez
Dr. Javier Civit Masot
Dr. Luis Muñoz
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. 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
  • healthcare
  • wellness care
  • eHealth
  • biomedical signals
  • data privacy and security protocols
  • explainable AI

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Related Special Issue

Published Papers (7 papers)

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Research

21 pages, 3367 KiB  
Article
Optimized Edge-Cloud System for Activity Monitoring Using Knowledge Distillation
by Daniel Deniz, Eduardo Ros, Eva M. Ortigosa and Francisco Barranco
Electronics 2024, 13(23), 4786; https://doi.org/10.3390/electronics13234786 - 4 Dec 2024
Viewed by 922
Abstract
Driven by the increasing care needs of residents in long-term care facilities, Ambient Assisted Living paradigms have become very popular, offering new solutions to alleviate this burden. This work proposes an efficient edge-cloud system for indoor activity monitoring in long-term care institutions. Action [...] Read more.
Driven by the increasing care needs of residents in long-term care facilities, Ambient Assisted Living paradigms have become very popular, offering new solutions to alleviate this burden. This work proposes an efficient edge-cloud system for indoor activity monitoring in long-term care institutions. Action recognition from video streams is implemented via Deep Learning networks running at edge nodes. Edge Computing stands out for its power efficiency, reduction in data transmission bandwidth, and inherent protection of residents’ sensitive data. To implement Artificial Intelligence models on these resource-limited edge nodes, complex Deep Learning networks are first distilled. Knowledge distillation allows for more accurate and efficient neural networks, boosting recognition performance of the solution by up to 8% without impacting resource usage. Finally, the central server runs a Quality and Resource Management (QRM) tool that monitors hardware qualities and recognition performance. This QRM tool performs runtime resource load balancing among the local processing devices ensuring real-time operation and optimized energy consumption. Also, the QRM module conducts runtime reconfiguration switching the running neural network to optimize the use of resources at the node and to improve the overall recognition, especially for critical situations such as falls. As part of our contributions, we also release the manually curated Indoor Action Dataset. Full article
(This article belongs to the Special Issue IoT for Healthcare and Wellbeing: Trends, Challenges, and Applications, 2nd Edition)
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14 pages, 10717 KiB  
Article
Diagnosis Aid System for Colorectal Cancer Using Low Computational Cost Deep Learning Architectures
by Álvaro Gago-Fabero, Luis Muñoz-Saavedra, Javier Civit-Masot, Francisco Luna-Perejón, José María Rodríguez Corral and Manuel Domínguez-Morales
Electronics 2024, 13(12), 2248; https://doi.org/10.3390/electronics13122248 - 7 Jun 2024
Cited by 3 | Viewed by 1411
Abstract
Colorectal cancer is the second leading cause of cancer-related deaths worldwide. To prevent deaths, regular screenings with histopathological analysis of colorectal tissue should be performed. A diagnostic aid system could reduce the time required by medical professionals, and provide an initial approach to [...] Read more.
Colorectal cancer is the second leading cause of cancer-related deaths worldwide. To prevent deaths, regular screenings with histopathological analysis of colorectal tissue should be performed. A diagnostic aid system could reduce the time required by medical professionals, and provide an initial approach to the final diagnosis. In this study, we analyze low computational custom architectures, based on Convolutional Neural Networks, which can serve as high-accuracy binary classifiers for colorectal cancer screening using histopathological images. For this purpose, we carry out an optimization process to obtain the best performance model in terms of effectiveness as a classifier and computational cost by reducing the number of parameters. Subsequently, we compare the results obtained with previous work in the same field. Cross-validation reveals a high robustness of the models as classifiers, yielding superior accuracy outcomes of 99.4 ± 0.58% and 93.2 ± 1.46% for the lighter model. The classifiers achieved an accuracy exceeding 99% on the test subset using low-resolution images and a significantly reduced layer count, with images sized at 11% of those used in previous studies. Consequently, we estimate a projected reduction of up to 50% in computational costs compared to the most lightweight model proposed in the existing literature. Full article
(This article belongs to the Special Issue IoT for Healthcare and Wellbeing: Trends, Challenges, and Applications, 2nd Edition)
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22 pages, 656 KiB  
Article
Enhancing Precision of Telemonitoring of COVID-19 Patients through Expert System Based on IoT Data Elaboration
by Martina Olivelli, Massimiliano Donati, Annamaria Vianello, Ilaria Petrucci, Stefano Masi, Alessio Bechini and Luca Fanucci
Electronics 2024, 13(8), 1462; https://doi.org/10.3390/electronics13081462 - 12 Apr 2024
Cited by 2 | Viewed by 1638
Abstract
The emergence of the highly contagious coronavirus disease has led to multiple pandemic waves, resulting in a significant number of hospitalizations and fatalities. Even outside of hospitals, general practitioners have faced serious challenges, stretching their resources and putting themselves at risk of infection. [...] Read more.
The emergence of the highly contagious coronavirus disease has led to multiple pandemic waves, resulting in a significant number of hospitalizations and fatalities. Even outside of hospitals, general practitioners have faced serious challenges, stretching their resources and putting themselves at risk of infection. Telemonitoring systems based on Internet of things technology have emerged as valuable tools for remotely monitoring disease progression, facilitating rapid intervention, and reducing the risk of hospitalization and mortality. They allow for personalized monitoring strategies and tailored treatment plans, which are crucial for improving health outcomes. However, determining the appropriate monitoring intensity remains the responsibility of physicians, which poses challenges and impacts their workload, and thus, can hinder timely responses. To address these challenges, this paper proposes an expert system designed to recommend and adjust the monitoring intensity for COVID-19 patients receiving home treatment based on their medical history, vital signs, and reported symptoms. The system underwent initial validation using real-world cases, demonstrating a favorable performance (F1-score of 0.85). Subsequently, once integrated with an Internet of Things telemonitoring system, a clinical trial will assess the system’s reliability in creating telemonitoring plans comparable with those of medics, evaluate its effectiveness in reducing medic–patient interactions or hospitalizations, and gauge patient satisfaction and safety. Full article
(This article belongs to the Special Issue IoT for Healthcare and Wellbeing: Trends, Challenges, and Applications, 2nd Edition)
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26 pages, 2521 KiB  
Article
Cloud-Connected Bracelet for Continuous Monitoring of Parkinson’s Disease Patients: Integrating Advanced Wearable Technologies and Machine Learning
by Asma Channa, Giuseppe Ruggeri, Rares-Cristian Ifrim, Nadia Mammone, Antonio Iera and Nirvana Popescu
Electronics 2024, 13(6), 1002; https://doi.org/10.3390/electronics13061002 - 7 Mar 2024
Cited by 5 | Viewed by 3180
Abstract
Parkinson’s disease (PD) is one of the most unremitting and dynamic neurodegenerative human diseases. Various wearable IoT devices have emerged for detecting, diagnosing, and quantifying PD, predominantly utilizing inertial sensors and computational algorithms. However, their proliferation poses novel challenges concerning security, privacy, connectivity, [...] Read more.
Parkinson’s disease (PD) is one of the most unremitting and dynamic neurodegenerative human diseases. Various wearable IoT devices have emerged for detecting, diagnosing, and quantifying PD, predominantly utilizing inertial sensors and computational algorithms. However, their proliferation poses novel challenges concerning security, privacy, connectivity, and power optimization. Clinically, continuous monitoring of patients’ motor function is imperative for optimizing Levodopa (L-dopa) dosage while mitigating adverse effects and motor activity decline. Tracking motor function alterations between visits is challenging, risking erroneous clinical decisions. Thus, there is a pressing need to furnish medical professionals with an ecosystem facilitating comprehensive Parkinson’s stage evaluation and disease progression monitoring, particularly regarding tremor and bradykinesia. This study endeavors to establish a holistic ecosystem centered around an energy-efficient Wi-Fi-enabled wearable bracelet dubbed A-WEAR. A-WEAR functions as a data collection conduit for Parkinson’s-related motion data, securely transmitting them to the Cloud for storage, processing, and severity estimation via bespoke learning algorithms. The experimental results demonstrate the resilience and effectiveness of the suggested technique, with 86.4% accuracy for bradykinesia and 90.9% accuracy for tremor estimation, along with good sensitivity and specificity for each scoring class. The recommended approach will support the timely determination of the severity of PD and ongoing patient activity monitoring. The system helps medical practitioners in decision making when initially assessing patients with PD and reviewing their progress and the effects of any treatment. Full article
(This article belongs to the Special Issue IoT for Healthcare and Wellbeing: Trends, Challenges, and Applications, 2nd Edition)
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16 pages, 4435 KiB  
Article
Application of Cognitive Information Systems in Medical Image Semantic Analysis
by Marek R. Ogiela and Lidia Ogiela
Electronics 2024, 13(2), 325; https://doi.org/10.3390/electronics13020325 - 12 Jan 2024
Viewed by 1355
Abstract
Cognitive information systems create a new class of intelligent systems focused on semantic data analysis tasks. Such systems are based on cognitive resonance processes, which use a knowledge-based perception model, to analyze and semantically classify visual data. Such systems can therefore be used [...] Read more.
Cognitive information systems create a new class of intelligent systems focused on semantic data analysis tasks. Such systems are based on cognitive resonance processes, which use a knowledge-based perception model, to analyze and semantically classify visual data. Such systems can therefore be used for image analysis and classification, including semantic analysis of medical images, aimed at supporting diagnostic processes and determining the severity of lesions visualized by diagnostic imaging methods. This paper will describe various types of cognitive information systems designed for lesion recognition in selected abdominal and coronary structures, as well as skeletal parts of the human body, made visible by the application of various modalities in medical diagnostic imaging procedures. In this paper, a new generation of cognitive systems will also be described, and when compared to existing systems, will have the ability to perform extended cognitive resonance processes. Inference based on extended resonance inference allows the system to acquire additional knowledge, as well as expand the knowledge base used for semantic analysis. This paper will also propose the implementation of new efficient formal grammars, which increase the efficiency of lesion recognition in selected medical images to over 90%. Full article
(This article belongs to the Special Issue IoT for Healthcare and Wellbeing: Trends, Challenges, and Applications, 2nd Edition)
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23 pages, 13352 KiB  
Article
Analysis of the Relationship between Personality Traits and Driving Stress Using a Non-Intrusive Wearable Device
by Wilhelm Daniel Scherz, Victor Corcoba, David Melendi, Ralf Seepold, Natividad Martínez Madrid and Juan Antonio Ortega
Electronics 2024, 13(1), 159; https://doi.org/10.3390/electronics13010159 - 29 Dec 2023
Cited by 3 | Viewed by 2009
Abstract
While driving, stress is caused by situations in which the driver estimates their ability to manage the driving demands as insufficient or loses the capability to handle the situation. This leads to increased numbers of driver mistakes and traffic violations. Additional stressing factors [...] Read more.
While driving, stress is caused by situations in which the driver estimates their ability to manage the driving demands as insufficient or loses the capability to handle the situation. This leads to increased numbers of driver mistakes and traffic violations. Additional stressing factors are time pressure, road conditions, or dislike for driving. Therefore, stress affects driver and road safety. Stress is classified into two categories depending on its duration and the effects on the body and psyche: short-term eustress and constantly present distress, which causes degenerative effects. In this work, we focus on distress. Wearable sensors are handy tools for collecting biosignals like heart rate, activity, etc. Easy installation and non-intrusive nature make them convenient for calculating stress. This study focuses on the investigation of stress and its implications. Specifically, the research conducts an analysis of stress within a select group of individuals from both Spain and Germany. The primary objective is to examine the influence of recognized psychological factors, including personality traits such as neuroticism, extroversion, psychoticism, stress and road safety. The estimation of stress levels was accomplished through the collection of physiological parameters (R-R intervals) using a Polar H10 chest strap. We observed that personality traits, such as extroversion, exhibited similar trends during relaxation, with an average heart rate 6% higher in Spain and 3% higher in Germany. However, while driving, introverts, on average, experienced more stress, with rates 4% and 1% lower than extroverts in Spain and Germany, respectively. Full article
(This article belongs to the Special Issue IoT for Healthcare and Wellbeing: Trends, Challenges, and Applications, 2nd Edition)
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13 pages, 414 KiB  
Article
Human Body as a Signal Transmission Medium for Body-Coupled Communication: Galvanic-Mode Models
by Vladimir Aristov and Atis Elsts
Electronics 2023, 12(21), 4550; https://doi.org/10.3390/electronics12214550 - 6 Nov 2023
Cited by 1 | Viewed by 3113
Abstract
Signal propagation models play a fundamental role in radio frequency communication research. However, emerging communication methods, such as body-coupled communication (BCC), require the creation of new models. In this paper, we introduce mathematical models that approximate the human body as an electrical circuit, [...] Read more.
Signal propagation models play a fundamental role in radio frequency communication research. However, emerging communication methods, such as body-coupled communication (BCC), require the creation of new models. In this paper, we introduce mathematical models that approximate the human body as an electrical circuit, as well as linear regression- and random forest-based predictive models that infer the expected signal loss from its frequency, measurement point locations, and body parameters. The results demonstrate a close correspondence between the amplitude-frequency response (AFR) predicted by the electrical circuit models and the experimental data gathered from volunteers. The accuracy of our predictive models was assessed by using their root mean square errors (RMSE), ranging from 1.5 to 7 dB depending on the signal frequency within the 0.05 to 20 MHz range. These results allow researchers and engineers to simulate and forecast the expected signal loss within BCC systems during their design phase. Full article
(This article belongs to the Special Issue IoT for Healthcare and Wellbeing: Trends, Challenges, and Applications, 2nd Edition)
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