IoT for Healthcare and Wellbeing: Trends, Challenges, and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 46200

Special Issue Editors

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
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
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

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) systems are covering a growing number of application areas which 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.

In 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
Guest Editors

Manuscript Submission Information

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Keywords

  • IoT
  • Healthcare
  • Wellness care
  • eHealth
  • Biomedical signals
  • Data privacy and security protocols
  • Explainable AI

Related Special Issue

Published Papers (15 papers)

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Research

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16 pages, 2663 KiB  
Article
Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept
by Marco Cascella, Vincenzo Norman Vitale, Michela D’Antò, Arturo Cuomo, Francesco Amato, Maria Romano and Alfonso Maria Ponsiglione
Electronics 2023, 12(17), 3716; https://doi.org/10.3390/electronics12173716 - 02 Sep 2023
Viewed by 984
Abstract
Perception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. [...] Read more.
Perception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. In this scenario, objective pain assessment is an open research challenge. This work introduces a framework for automatic pain assessment. The proposed method is based on a wearable biosignal platform to extract quantitative indicators of the patient pain experience, evaluated through a self-assessment report. Two preliminary case studies focused on the simultaneous acquisition of electrocardiography (ECG), electrodermal activity (EDA), and accelerometer signals are illustrated and discussed. The results demonstrate the feasibility of the approach, highlighting the potential of EDA in capturing skin conductance responses (SCR) related to pain events in chronic cancer pain. A weak correlation (R = 0.2) is found between SCR parameters and the standard deviation of the interbeat interval series (SDRR), selected as the Heart Rate Variability index. A statistically significant (p < 0.001) increase in both EDA signal and SDRR is detected in movement with respect to rest conditions (assessed by means of the accelerometer signals) in the case of motion-associated cancer pain, thus reflecting the relationship between motor dynamics, which trigger painful responses, and the subsequent activation of the autonomous nervous system. With the objective of integrating parameters obtained from biosignals to establish pain signatures within different clinical scenarios, the proposed framework proves to be a promising research approach to define pain signatures in different clinical contexts. Full article
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28 pages, 582 KiB  
Article
Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey
by Fatemeh Mosaiyebzadeh, Seyedamin Pouriyeh, Reza M. Parizi, Quan Z. Sheng, Meng Han, Liang Zhao, Giovanna Sannino, Caetano Mazzoni Ranieri, Jó Ueyama and Daniel Macêdo Batista
Electronics 2023, 12(12), 2703; https://doi.org/10.3390/electronics12122703 - 16 Jun 2023
Cited by 5 | Viewed by 1451
Abstract
Advancements in wearable medical devices using the IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), efficient healthcare services can be provided to patients. Healthcare professionals have effectively used AI-based models to analyze the [...] Read more.
Advancements in wearable medical devices using the IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), efficient healthcare services can be provided to patients. Healthcare professionals have effectively used AI-based models to analyze the data collected from IoHT devices to treat various diseases. Data must be processed and analyzed while avoiding privacy breaches, in compliance with legal rules and regulations, such as the HIPAA and GDPR. Federated learning (FL) is a machine learning-based approach allowing multiple entities to train an ML model collaboratively without sharing their data. It is particularly beneficial in healthcare, where data privacy and security are substantial concerns. Even though FL addresses some privacy concerns, there is still no formal proof of privacy guarantees for IoHT data. Privacy-enhancing technologies (PETs) are tools and techniques designed to enhance the privacy and security of online communications and data sharing. PETs provide a range of features that help protect users’ personal information and sensitive data from unauthorized access and tracking. This paper comprehensively reviews PETs concerning FL in the IoHT scenario and identifies several key challenges for future research. Full article
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20 pages, 813 KiB  
Article
A Validation Study to Confirm the Accuracy of Wearable Devices Based on Health Data Analysis
by Nikola Hrabovska, Erik Kajati and Iveta Zolotova
Electronics 2023, 12(11), 2536; https://doi.org/10.3390/electronics12112536 - 04 Jun 2023
Cited by 3 | Viewed by 2369
Abstract
This research article presents an analysis of health data collected from wearable devices, aiming to uncover the practical applications and implications of such analyses in personalized healthcare. The study explores insights derived from heart rate, sleep patterns, and specific workouts. The findings demonstrate [...] Read more.
This research article presents an analysis of health data collected from wearable devices, aiming to uncover the practical applications and implications of such analyses in personalized healthcare. The study explores insights derived from heart rate, sleep patterns, and specific workouts. The findings demonstrate potential applications in personalized health monitoring, fitness optimization, and sleep quality assessment. The analysis focused on the heart rate, sleep patterns, and specific workouts of the respondents. Results indicated that heart rate values during functional strength training fell within the target zone, with variations observed between different types of workouts. Sleep patterns were found to be individualized, with variations in sleep interruptions among respondents. The study also highlighted the impact of individual factors, such as demographics and manually defined information, on workout outcomes. The study acknowledges the challenges posed by the emerging nature of wearable devices and technological constraints. However, it emphasizes the significance of the research, highlighting variations in workout intensities based on heart rate data and the individualized nature of sleep patterns and disruptions. Perhaps the future cognitive healthcare platform may harness these insights to empower individuals in monitoring their health and receiving personalized recommendations for improved well-being. This research opens up new horizons in personalized healthcare, transforming how we approach health monitoring and management. Full article
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28 pages, 10169 KiB  
Article
Magnetic Localization of Wireless Ingestible Capsules Using a Belt-Shaped Array Transmitter
by Ivan Castro, Jan Willem de Wit, Jasper van Vooren, Tom Van Quaethem, Weixi Huang and Tom Torfs
Electronics 2023, 12(10), 2217; https://doi.org/10.3390/electronics12102217 - 12 May 2023
Viewed by 1037
Abstract
In the last 20 years, research into and clinical use of wireless ingestible capsules (WIC) has increased, with capsule endoscopy being the most common application in clinical practice. Additionally, there has been an increased research interest in sensing capsules. To maximize the usefulness [...] Read more.
In the last 20 years, research into and clinical use of wireless ingestible capsules (WIC) has increased, with capsule endoscopy being the most common application in clinical practice. Additionally, there has been an increased research interest in sensing capsules. To maximize the usefulness of the information provided by these devices, it is crucial to know their location within the gastrointestinal tract. The main WIC localization methods in research include radio frequency approaches, video-based methods, and magnetic-based methods. Of these methods, the magnetic-based methods show the most potential in terms of localization accuracy. However, the need for an external transmitting (or sensing) array poses an important limitation, as evidenced by most of the reported methods involving a rigid structure. This poses a challenge to its wearability and performance in daily life environments. This paper provides an overview of the state of the art on magnetic-based localization for WIC, followed by a proof of concept of a system that aims to solve the wearability challenges. Comparative performance simulations of different transmitter arrays are presented. The effect of including one or two receiver coils in the WIC is also evaluated in the simulation. Experimental localization results for a planar transmitter array and for a more wearable belt-shaped transmitter are presented and compared. A localization mean absolute error (MAE) as low as 6.5 mm was achieved for the planar array in a volume of 15 cm × 15 cm × 15 cm, starting at a 5 cm distance from the transmitter. Evaluating the belt array in a similar volume of interest (15 cm × 15 cm × 15 cm starting at 7.5 cm distance from the transmitter) resulted in an MAE of 13.1 mm across the volume and a plane-specific MAE as low as 9.5 mm when evaluated at a 12.5 cm distance. These initial results demonstrate comparable performances between these two transmitters, while the belt array has the potential to enable measurements in daily-life environments. Despite these promising results, it was identified that an improvement in the model for the magnetic field when using transmitter coils with ferrite cores is necessary and is likely to result in better localization accuracy. This belt-array approach, together with compensation techniques for body motion, as recently reported for rigid arrays, has the potential to enable WIC localization in uncontrolled environments with minimal impact on the user’s daily life. Full article
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16 pages, 1610 KiB  
Article
Deep Learning and Cloud-Based Computation for Cervical Spine Fracture Detection System
by Paweł Chłąd and Marek R. Ogiela
Electronics 2023, 12(9), 2056; https://doi.org/10.3390/electronics12092056 - 29 Apr 2023
Cited by 2 | Viewed by 1766
Abstract
Modern machine learning models, such as vision transformers (ViT), have been shown to outperform convolutional neural networks (CNNs) while using fewer computational resources. Although computed tomography (CT) is now the standard for imaging diagnosis of adult spine fractures, analyzing CT scans by hand [...] Read more.
Modern machine learning models, such as vision transformers (ViT), have been shown to outperform convolutional neural networks (CNNs) while using fewer computational resources. Although computed tomography (CT) is now the standard for imaging diagnosis of adult spine fractures, analyzing CT scans by hand is both time consuming and error prone. Deep learning (DL) techniques can offer more effective methods for detecting fractures, and with the increasing availability of ubiquitous cloud resources, implementing such systems worldwide is becoming more feasible. This study aims to evaluate the effectiveness of ViT for detecting cervical spine fractures. Data gathered during the research indicates that ViT models are suitable for large-scale automatic detection system implementation. The model achieved 98% accuracy and was easy to train while also being easily explainable. Full article
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25 pages, 7577 KiB  
Article
IoT System for Gluten Prediction in Flour Samples Using NIRS Technology, Deep and Machine Learning Techniques
by Oscar Jossa-Bastidas, Ainhoa Osa Sanchez, Leire Bravo-Lamas and Begonya Garcia-Zapirain
Electronics 2023, 12(8), 1916; https://doi.org/10.3390/electronics12081916 - 18 Apr 2023
Cited by 2 | Viewed by 1915
Abstract
Gluten is a natural complex protein present in a variety of cereal grains, including species of wheat, barley, rye, triticale, and oat cultivars. When someone suffering from celiac disease ingests it, the immune system starts attacking its own tissues. Prevalence studies suggest that [...] Read more.
Gluten is a natural complex protein present in a variety of cereal grains, including species of wheat, barley, rye, triticale, and oat cultivars. When someone suffering from celiac disease ingests it, the immune system starts attacking its own tissues. Prevalence studies suggest that approximately 1% of the population may have gluten-related disorders during their lifetime, thus, the scientific community has tried to study different methods to detect this protein. There are multiple commercial quantitative methods for gluten detection, such as enzyme-linked immunosorbent assays (ELISAs), polymerase chain reactions, and advanced proteomic methods. ELISA-based methods are the most widely used; but despite being reliable, they also have certain constraints, such as the long periods they take to detect the protein. This study focuses on developing a novel, rapid, and budget-friendly IoT system using Near-infrared spectroscopy technology, Deep and Machine Learning algorithms to predict the presence or absence of gluten in flour samples. 12,053 samples were collected from 3 different types of flour (rye, corn, and oats) using an IoT prototype portable solution composed of a Raspberry Pi 4 and the DLPNIRNANOEVM infrared sensor. The proposed solution can collect, store, and predict new samples and is connected by using a real-time serverless architecture designed in the Amazon Web services. The results showed that the XGBoost classifier reached an Accuracy of 94.52% and an F2-score of 92.87%, whereas the Deep Neural network had an Accuracy of 91.77% and an F2-score of 96.06%. The findings also showed that it is possible to achieve high-performance results by only using the 1452–1583 nm wavelength range. The IoT prototype portable solution presented in this study not only provides a valuable contribution to the state of the art in the use of the NIRS + Artificial Intelligence in the food industry, but it also represents a first step towards the development of technologies that can improve the quality of life of people with food intolerances. Full article
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16 pages, 3299 KiB  
Article
LUVS-Net: A Lightweight U-Net Vessel Segmentor for Retinal Vasculature Detection in Fundus Images
by Muhammad Talha Islam, Haroon Ahmed Khan, Khuram Naveed, Ali Nauman, Sardar Muhammad Gulfam and Sung Won Kim
Electronics 2023, 12(8), 1786; https://doi.org/10.3390/electronics12081786 - 10 Apr 2023
Cited by 4 | Viewed by 1316
Abstract
This paper presents LUVS-Net, which is a lightweight convolutional network for retinal vessel segmentation in fundus images that is designed for resource-constrained devices that are typically unable to meet the computational requirements of large neural networks. The computational challenges arise due to low-quality [...] Read more.
This paper presents LUVS-Net, which is a lightweight convolutional network for retinal vessel segmentation in fundus images that is designed for resource-constrained devices that are typically unable to meet the computational requirements of large neural networks. The computational challenges arise due to low-quality retinal images, wide variance in image acquisition conditions and disparities in intensity. Consequently, the training of existing segmentation methods requires a multitude of trainable parameters for the training of networks, resulting in computational complexity. The proposed Lightweight U-Net for Vessel Segmentation Network (LUVS-Net) can achieve high segmentation performance with only a few trainable parameters. This network uses an encoder–decoder framework in which edge data are transposed from the first layers of the encoder to the last layer of the decoder, massively improving the convergence latency. Additionally, LUVS-Net’s design allows for a dual-stream information flow both inside as well as outside of the encoder–decoder pair. The network width is enhanced using group convolutions, which allow the network to learn a larger number of low- and intermediate-level features. Spatial information loss is minimized using skip connections, and class imbalances are mitigated using dice loss for pixel-wise classification. The performance of the proposed network is evaluated on the publicly available retinal blood vessel datasets DRIVE, CHASE_DB1 and STARE. LUVS-Net proves to be quite competitive, outperforming alternative state-of-the-art segmentation methods and achieving comparable accuracy using trainable parameters that are reduced by two to three orders of magnitude compared with those of comparative state-of-the-art methods. Full article
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19 pages, 5914 KiB  
Article
IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning
by A Angel Nancy, Dakshanamoorthy Ravindran, P M Durai Raj Vincent, Kathiravan Srinivasan and Daniel Gutierrez Reina
Electronics 2022, 11(15), 2292; https://doi.org/10.3390/electronics11152292 - 22 Jul 2022
Cited by 72 | Viewed by 10184
Abstract
The Internet of Things confers seamless connectivity between people and objects, and its confluence with the Cloud improves our lives. Predictive analytics in the medical domain can help turn a reactive healthcare strategy into a proactive one, with advanced artificial intelligence and machine [...] Read more.
The Internet of Things confers seamless connectivity between people and objects, and its confluence with the Cloud improves our lives. Predictive analytics in the medical domain can help turn a reactive healthcare strategy into a proactive one, with advanced artificial intelligence and machine learning approaches permeating the healthcare industry. As the subfield of ML, deep learning possesses the transformative potential for accurately analysing vast data at exceptional speeds, eliciting intelligent insights, and efficiently solving intricate issues. The accurate and timely prediction of diseases is crucial in ensuring preventive care alongside early intervention for people at risk. With the widespread adoption of electronic clinical records, creating prediction models with enhanced accuracy is key to harnessing recurrent neural network variants of deep learning possessing the ability to manage sequential time-series data. The proposed system acquires data from IoT devices, and the electronic clinical data stored on the cloud pertaining to patient history are subjected to predictive analytics. The smart healthcare system for monitoring and accurately predicting heart disease risk built around Bi-LSTM (bidirectional long short-term memory) showcases an accuracy of 98.86%, a precision of 98.9%, a sensitivity of 98.8%, a specificity of 98.89%, and an F-measure of 98.86%, which are much better than the existing smart heart disease prediction systems. Full article
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13 pages, 575 KiB  
Article
A Computational Framework for Cyber Threats in Medical IoT Systems
by Geetanjali Rathee, Hemraj Saini, Chaker Abdelaziz Kerrache and Jorge Herrera-Tapia
Electronics 2022, 11(11), 1705; https://doi.org/10.3390/electronics11111705 - 27 May 2022
Cited by 4 | Viewed by 1525
Abstract
Smart social systems are ones where a number of individuals share and interact with each other via various networking devices. There exist a number of benefits to including smart-based systems in networks such as religions, economy, medicine, and other networks. However, the involvement [...] Read more.
Smart social systems are ones where a number of individuals share and interact with each other via various networking devices. There exist a number of benefits to including smart-based systems in networks such as religions, economy, medicine, and other networks. However, the involvement of several cyber threats leads to adverse effects on society in terms of finance, business, liability, economy, psychology etc. The aim of this paper is to present a secure and efficient medical Internet of Things communication mechanism by preventing various cyber threats. The proposed framework uses Artificial Intelligence-based techniques such as Levenberg–Marquardt (LM) and Viterbi algorithms to prevent various social cyber threats during interaction and sharing of messages. The proposed mechanism is simulated and validated with various performance metrics compared with the traditional mechanism. Full article
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27 pages, 11116 KiB  
Article
An IoT Assisted Real-Time High CMRR Wireless Ambulatory ECG Monitoring System with Arrhythmia Detection
by Hassan Ali, Hein Htet Naing and Raziq Yaqub
Electronics 2021, 10(16), 1871; https://doi.org/10.3390/electronics10161871 - 04 Aug 2021
Cited by 12 | Viewed by 3453
Abstract
The absence of cardiovascular disease (CVD) diagnostic and management solutions cause significant morbidity among populations in rural areas and the coronavirus disease of 2019 (COVID-19) emergency. To tackle this problem, in this paper, the development of an Internet of things (IoT) assisted ambulatory [...] Read more.
The absence of cardiovascular disease (CVD) diagnostic and management solutions cause significant morbidity among populations in rural areas and the coronavirus disease of 2019 (COVID-19) emergency. To tackle this problem, in this paper, the development of an Internet of things (IoT) assisted ambulatory electrocardiogram (ECG) monitoring system is presented. The system’s wearable single-channel data acquisition device supports 25 h of continuous operation. A right leg drive (RLD) circuit supported analog frontend (AFE) with a high common mode rejection ratio (CMRR) of 121 dB and a digitally implemented notch filter is used to suppress power-line frequency interference. The wearable device continuously sends the collected ECG data via Bluetooth to the user’s smartphone. An application on the user’s smartphone renders real-time ECG trace and heart rate and detects abnormal heart rhythms. This data are then shared in real-time with the user’s doctor via a real-time cloud database. An application on the doctor’s smartphone allows real-time visualization of this data and detection of arrhythmias. Simulations and experimental results demonstrate that reliable ECG signals can be captured with low latency and the heart rate computation is comparable to a commercial application. Low cost, scalability, low latency, real-time ECG monitoring, and improved performance of the system make the system highly suitable for the real-time remote identification and management of CVDs in users of rural areas and in the COVID-19 pandemic. Full article
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15 pages, 2183 KiB  
Article
IoT Device for Sitting Posture Classification Using Artificial Neural Networks
by Francisco Luna-Perejón, Juan Manuel Montes-Sánchez, Lourdes Durán-López, Alberto Vazquez-Baeza, Isabel Beasley-Bohórquez and José L. Sevillano-Ramos
Electronics 2021, 10(15), 1825; https://doi.org/10.3390/electronics10151825 - 29 Jul 2021
Cited by 14 | Viewed by 2446
Abstract
Nowadays, the percentage of time that the population spends sitting has increased substantially due to the use of computers as the main tool for work or leisure and the increase in jobs with a high office workload. As a consequence, it is common [...] Read more.
Nowadays, the percentage of time that the population spends sitting has increased substantially due to the use of computers as the main tool for work or leisure and the increase in jobs with a high office workload. As a consequence, it is common to suffer musculoskeletal pain, mainly in the back, which can lead to both temporary and chronic damage. This pain is related to holding a posture during a prolonged period of sitting, usually in front of a computer. This work presents a IoT posture monitoring system while sitting. The system consists of a device equipped with Force Sensitive Resistors (FSR) that, placed on a chair seat, detects the points where the user exerts pressure when sitting. The system is complemented with a Machine Learning model based on Artificial Neural Networks, which was trained to recognize the neutral correct posture as well as the six most frequent postures that involve risk of damage to the locomotor system. In this study, data was collected from 12 participants for each of the seven positions considered, using the developed sensing device. Several neural network models were trained and evaluated in order to improve the classification effectiveness. Hold-Out technique was used to guide the training and evaluation process. The results achieved a mean accuracy of 81% by means of a model consisting of two hidden layers of 128 neurons each. These results demonstrate that is feasible to distinguish different sitting postures using few sensors allocated in the surface of a seat, which implies lower costs and less complexity of the system. Full article
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12 pages, 1160 KiB  
Article
Integration and Applications of Fog Computing and Cloud Computing Based on the Internet of Things for Provision of Healthcare Services at Home
by Muhammad Ijaz, Gang Li, Ling Lin, Omar Cheikhrouhou, Habib Hamam and Alam Noor
Electronics 2021, 10(9), 1077; https://doi.org/10.3390/electronics10091077 - 02 May 2021
Cited by 33 | Viewed by 3574
Abstract
Due to the COVID-19 pandemic, the world has faced a significant challenge in the increase of the rate of morbidity and mortality among people, particularly the elderly aged patients. The risk of acquiring infections may increase during the visit of patients to the [...] Read more.
Due to the COVID-19 pandemic, the world has faced a significant challenge in the increase of the rate of morbidity and mortality among people, particularly the elderly aged patients. The risk of acquiring infections may increase during the visit of patients to the hospitals. The utilisation of technology such as the “Internet of Things (IoT)” based on Fog Computing and Cloud Computing turned out to be efficient in enhancing the healthcare quality services for the patients. The present paper aims at gaining a better understanding and insights into the most effective and novel IoT-based applications such as Cloud Computing and Fog Computing and their implementations in the healthcare field. The research methodology employed the collection of the information from the databases such as PubMed, Google Scholar, MEDLINE, and Science Direct. There are five research articles selected after 2015 based on the inclusion and exclusion criteria set for the study. The findings of the studies included in this paper indicate that IoT-based Fog Computing and Cloud Computing increase the delivery of healthcare quality services to patients. The technology showed high efficiency in terms of convenience, reliability, safety, and cost-effectiveness. Future studies are required to incorporate the models that provided the best quality services using the Fog and Cloud Computation techniques for the different user requirements. Moreover, edge computing could be used to significantly enhance the provision of health services at home. Full article
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17 pages, 6965 KiB  
Article
Wearable Wireless Physiological Monitoring System Based on Multi-Sensor
by Hongru Li, Guiling Sun, Yue Li and Runzhuo Yang
Electronics 2021, 10(9), 986; https://doi.org/10.3390/electronics10090986 - 21 Apr 2021
Cited by 8 | Viewed by 3010
Abstract
The purpose of wearable technology is to use multimedia, sensors, and wireless communication to integrate specific technology into user clothes or accessories. With the help of various sensors, the physiological monitoring system can collect, process, and transmit physiological signals without causing damage. Wearable [...] Read more.
The purpose of wearable technology is to use multimedia, sensors, and wireless communication to integrate specific technology into user clothes or accessories. With the help of various sensors, the physiological monitoring system can collect, process, and transmit physiological signals without causing damage. Wearable technology has been widely used in patient monitoring and people’s health management because of its low-load, mobile, and easy-to-use characteristics, and it supports long-term continuous work and can carry out wireless transmissions. In this paper, we established a Wi-Fi-based physiological monitoring system that can accurately measure heart rate, body surface temperature, and motion data and can quickly detect and alert the user about abnormal heart rates. Full article
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Review

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30 pages, 764 KiB  
Review
Reviewing Multimodal Machine Learning and Its Use in Cardiovascular Diseases Detection
by Mohammad Moshawrab, Mehdi Adda, Abdenour Bouzouane, Hussein Ibrahim and Ali Raad
Electronics 2023, 12(7), 1558; https://doi.org/10.3390/electronics12071558 - 26 Mar 2023
Cited by 8 | Viewed by 4797
Abstract
Machine Learning (ML) and Deep Learning (DL) are derivatives of Artificial Intelligence (AI) that have already demonstrated their effectiveness in a variety of domains, including healthcare, where they are now routinely integrated into patients’ daily activities. On the other hand, data heterogeneity has [...] Read more.
Machine Learning (ML) and Deep Learning (DL) are derivatives of Artificial Intelligence (AI) that have already demonstrated their effectiveness in a variety of domains, including healthcare, where they are now routinely integrated into patients’ daily activities. On the other hand, data heterogeneity has long been a key obstacle in AI, ML and DL. Here, Multimodal Machine Learning (Multimodal ML) has emerged as a method that enables the training of complex ML and DL models that use heterogeneous data in their learning process. In addition, Multimodal ML enables the integration of multiple models in the search for a single, comprehensive solution to a complex problem. In this review, the technical aspects of Multimodal ML are discussed, including a definition of the technology and its technical underpinnings, especially data fusion. It also outlines the differences between this technology and others, such as Ensemble Learning, as well as the various workflows that can be followed in Multimodal ML. In addition, this article examines in depth the use of Multimodal ML in the detection and prediction of Cardiovascular Diseases, highlighting the results obtained so far and the possible starting points for improving its use in the aforementioned field. Finally, a number of the most common problems hindering the development of this technology and potential solutions that could be pursued in future studies are outlined. Full article
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Other

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26 pages, 3394 KiB  
Systematic Review
A Systematic Literature Review of Enabling IoT in Healthcare: Motivations, Challenges, and Recommendations
by Huda Hussein Mohamad Jawad, Zainuddin Bin Hassan, Bilal Bahaa Zaidan, Farah Hussein Mohammed Jawad, Duha Husein Mohamed Jawad and Wajdi Hamza Dawod Alredany
Electronics 2022, 11(19), 3223; https://doi.org/10.3390/electronics11193223 - 08 Oct 2022
Cited by 9 | Viewed by 4025
Abstract
Internet of things (IoT) has revolutionized how we utilize technology over the past decade. IoT’s rapid growth affects several fields, including the healthcare sector. As a result, the concept of smart healthcare or electronic healthcare (e-healthcare) has emerged. Smart healthcare promises to enhance [...] Read more.
Internet of things (IoT) has revolutionized how we utilize technology over the past decade. IoT’s rapid growth affects several fields, including the healthcare sector. As a result, the concept of smart healthcare or electronic healthcare (e-healthcare) has emerged. Smart healthcare promises to enhance people’s lives and wellbeing by monitoring them, offering an efficient connection, improving mobility, gathering medical data, and decreasing hospital and patient costs. IoT in healthcare is still one of the hot and trendy topics that needs in-depth investigation. No recent review has been conducted to elucidate the extent of research in the area, features of published papers, motives, and challenges in enabling IoT in healthcare systems. This study presents a comprehensive systematic review of the screened articles published between 2015 and 2022 pertaining to enabling IoT in healthcare services and applications. A total of 106 papers fulfilled the final inclusion criteria and were analyzed using systematic literature review (SLR). Two procedures were used to review the final articles: First, publications are examined in terms of study designs, publishing journals, and topics/study objectives. In the second approach, motives, challenges, and recommendations for enabling IoT in healthcare systems are explored. This article summarizes published studies on IoT in healthcare systems and its usage in smart healthcare service delivery. Based on the reviewed studies, recommendations for future research to enable the effective application of IoT in healthcare and service delivery are proposed. Full article
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