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Advances in IoMT for Healthcare Systems
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Advances in IoMT for Healthcare Systems

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 40086

Special Issue Editors

Prof. Dr. Jin-Ghoo Choi

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
Interests: wireless sensor networks; Internet-of-Things; mobile and wireless networks
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Shafiq

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
Interests: Internet of Things; vehicle-to-everything communication; smart cities; machine learning, computational intelligence; data science; human factors engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Habib Hamam

E-Mail Website
Guest Editor
Faculty of Engineering, Moncton University, Moncton, NB E1A3E9, Canada
Interests: optical telecommunications; wireless Communications; diffraction; fiber components; RFID; information processing; data protection; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Omar Cheikhrouhou

E-Mail Website
Guest Editor
Higher Institute of Computer Science of Mahdia, University of Monastir, Tunisia (ISIMA), Sidi Massoud -BP 49, Mahdia, Tunisia
Interests: security; blockchain; cloud computing; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of micro-computing devices such as sensors, actuators, RFID tags, and machine-to-machine (M2M) communications has enabled new Internet of Things (IoT) solutions to reshape many network applications. One example of this is the healthcare system, which has been revolutionized by the IoT solutions by the introduction of a branch of the Internet of Medical Things (IoMT) system. The IoMT solution has attracted a great deal of attention because of its ability to autonomously acquire, analyze, and share data on the Internet with the help of micro-computing medical devices, data-processing algorithms, and supporting communication protocols. Modern healthcare services widely use IoMT applications to remotely monitor patients with chronic diseases and improve quality of life. However, the adoption of IoMT faces many challenges, such as the interoperability between medical systems, the design of wearable (and/or ambient) medical devices, medical data analysis, the security and privacy of patient records, and the interaction between patients and medical devices. In this regard, modern computing technologies (such as machine learning, cloud/edge computing, and data mining), soft computing methods, and dedicated communication protocols may help provide services for the IoMT system to improve timely patient diagnosis; improve disease control, treatment methods, and drug management; and improve the user experience of the patient and medical staff.

The purpose of this Special Issue is to provide the latest reference materials on theoretical and practical challenges, as well as innovative ideas and solutions for IoMT for healthcare systems. The scope of this Special issue includes (but is not limited to): high-performance resilient infrastructure for IoMT systems; ontology-based recommendation and disease identification systems; innovative wearable and/or ambient IoMT devices; collection, modeling, and evaluation of big data for IoMT systems; in-hospital and in-home healthcare functions for IoMT systems; M2M interoperability and communication protocols for IoMT systems; optimized data security, privacy, and trust for IoMT systems; AI-based IoMT in telehealth virtual consulting and patient monitoring; innovative human–computer interaction models for IoMT systems; legal, ethical, and social considerations in IoMT for healthcare systems; AI-based signal and image processing applied to health; data analysis for health issues; EEG signals and systems.

Prof. Dr. Jin-Ghoo Choi
Dr. Muhammad Shafiq
Prof. Dr. Habib Hamam
Dr. Omar Cheikhrouhou
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

  • Internet of Medical Things
  • smart healthcare
  • telehealth virtual consulting
  • remote pain/patient monitoring
  • Medical Big Data Analytics
  • EEG signals
  • Artificial Intelligence applied to health
  • Data Analysis
  • RFID

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Published Papers (15 papers)

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Editorial

Jump to: Research, Review

6 pages, 178 KiB  
Editorial
Advances in IoMT for Healthcare Systems
by Muhammad Shafiq, Jin-Ghoo Choi, Omar Cheikhrouhou and Habib Hamam
Sensors 2024, 24(1), 10; https://doi.org/10.3390/s24010010 - 19 Dec 2023
Viewed by 716
Abstract
Nowadays, the demand for healthcare to transform from traditional hospital and disease-centered services to smart healthcare and patient-centered services, including the health management, biomedical diagnosis, and remote monitoring of patients with chronic diseases, is growing tremendously [...] Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)

Research

Jump to: Editorial, Review

15 pages, 2760 KiB  
Article
FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas
by J. V. Bibal Benifa, Channabasava Chola, Abdullah Y. Muaad, Mohd Ammar Bin Hayat, Md Belal Bin Heyat, Rajat Mehrotra, Faijan Akhtar, Hany S. Hussein, Debora Libertad Ramírez Vargas, Ángel Kuc Castilla, Isabel de la Torre Díez and Salabat Khan
Sensors 2023, 23(13), 6090; https://doi.org/10.3390/s23136090 - 02 Jul 2023
Cited by 5 | Viewed by 2428
Abstract
A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate the face mask protocol in public places. To achieve this goal, a private dataset was created, including different face images with and without [...] Read more.
A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate the face mask protocol in public places. To achieve this goal, a private dataset was created, including different face images with and without masks. The proposed model was trained to detect face masks from real-time surveillance videos. The proposed face mask detection (FMDNet) model achieved a promising detection of 99.0% in terms of accuracy for identifying violations (no face mask) in public places. The model presented a better detection capability compared to other recent DL models such as FSA-Net, MobileNet V2, and ResNet by 24.03%, 5.0%, and 24.10%, respectively. Meanwhile, the model is lightweight and had a confidence score of 99.0% in a resource-constrained environment. The model can perform the detection task in real-time environments at 41.72 frames per second (FPS). Thus, the developed model can be applicable and useful for governments to maintain the rules of the SOP protocol. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
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22 pages, 811 KiB  
Article
Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
by Nouf Abdullah Almujally, Turki Aljrees, Oumaima Saidani, Muhammad Umer, Zaid Bin Faheem, Nihal Abuzinadah, Khaled Alnowaiser and Imran Ashraf
Sensors 2023, 23(10), 4580; https://doi.org/10.3390/s23104580 - 09 May 2023
Cited by 8 | Viewed by 2365
Abstract
With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and [...] Read more.
With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources. In addition, it utilizes artificial intelligence (AI) approaches to control a large volume of data for better use, storing, managing, and making decisions. In this research, a health monitoring system based on AI and IoT is designed to deal with the data of heart patients. The system monitors the heart patient’s activities, which helps to inform patients about their health status. Moreover, the system can perform disease classification using machine learning models. Experimental results reveal that the proposed system can perform real-time monitoring of patients and classify diseases with higher accuracy. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
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21 pages, 6180 KiB  
Article
Interactive Cardio System for Healthcare Improvement
by Galya Georgieva-Tsaneva
Sensors 2023, 23(3), 1186; https://doi.org/10.3390/s23031186 - 20 Jan 2023
Viewed by 1486
Abstract
The paper presents an interactive cardio system that can be used to improve healthcare. The proposed system receives, processes, and analyzes cardio data using an Internet-based software platform. The system enables the acquisition of biomedical data using various means of recording cardiac signals [...] Read more.
The paper presents an interactive cardio system that can be used to improve healthcare. The proposed system receives, processes, and analyzes cardio data using an Internet-based software platform. The system enables the acquisition of biomedical data using various means of recording cardiac signals located in remote locations around the world. The recorded discretized cardio information is transmitted to the system for processing and mathematical analysis. At the same time, the recorded cardio data can also be stored online in established databases. The article presents the algorithms for the preprocessing and mathematical analysis of cardio data (heart rate variability). The results of studies conducted on the Holter recordings of healthy individuals and individuals with cardiovascular diseases are presented. The created system can be used for the remote monitoring of patients with chronic cardiovascular diseases or patients in remote settlements (where, for example, there may be no hospitals), control and assistance in the process of treatment, and monitoring the taking of prescribed drugs to help to improve people’s quality of life. In addition, the issue of ensuring the security of cardio information and the confidentiality of the personal data of health users is considered. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
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24 pages, 2085 KiB  
Article
Transfer Learning on Small Datasets for Improved Fall Detection
by Nader Maray, Anne Hee Ngu, Jianyuan Ni, Minakshi Debnath and Lu Wang
Sensors 2023, 23(3), 1105; https://doi.org/10.3390/s23031105 - 18 Jan 2023
Cited by 14 | Viewed by 3068
Abstract
Falls in the elderly are associated with significant morbidity and mortality. While numerous fall detection devices incorporating AI and machine learning algorithms have been developed, no known smartwatch-based system has been used successfully in real-time to detect falls for elderly persons. We have [...] Read more.
Falls in the elderly are associated with significant morbidity and mortality. While numerous fall detection devices incorporating AI and machine learning algorithms have been developed, no known smartwatch-based system has been used successfully in real-time to detect falls for elderly persons. We have developed and deployed a SmartFall system on a commodity-based smartwatch which has been trialled by nine elderly participants. The system, while being usable and welcomed by the participants in our trials, has two serious limitations. The first limitation is the inability to collect a large amount of personalized data for training. When the fall detection model, which is trained with insufficient data, is used in the real world, it generates a large amount of false positives. The second limitation is the model drift problem. This means an accurate model trained using data collected with a specific device performs sub-par when used in another device. Therefore, building one model for each type of device/watch is not a scalable approach for developing smartwatch-based fall detection system. To tackle those issues, we first collected three datasets including accelerometer data for fall detection problem from different devices: the Microsoft watch (MSBAND), the Huawei watch, and the meta-sensor device. After that, a transfer learning strategy was applied to first explore the use of transfer learning to overcome the small dataset training problem for fall detection. We also demonstrated the use of transfer learning to generalize the model across the heterogeneous devices. Our preliminary experiments demonstrate the effectiveness of transfer learning for improving fall detection, achieving an F1 score higher by over 10% on average, an AUC higher by over 0.15 on average, and a smaller false positive prediction rate than the non-transfer learning approach across various datasets collected using different devices with different hardware specifications. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
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16 pages, 3039 KiB  
Article
An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals
by Amna Waheed Awan, Syed Muhammad Usman, Shehzad Khalid, Aamir Anwar, Roobaea Alroobaea, Saddam Hussain, Jasem Almotiri, Syed Sajid Ullah and Muhammad Usman Akram
Sensors 2022, 22(23), 9480; https://doi.org/10.3390/s22239480 - 04 Dec 2022
Cited by 5 | Viewed by 2241
Abstract
Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR [...] Read more.
Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated autonomously from human central nervous system. Researchers have developed multiple methods for the classification of these signals for emotion detection. However, due to the non-linear nature of these signals and the inclusion of noise, while recording, accurate classification of physiological signals is a challenge for emotion charting. Valence and arousal are two important states for emotion detection; therefore, this paper presents a novel ensemble learning method based on deep learning for the classification of four different emotional states including high valence and high arousal (HVHA), low valence and low arousal (LVLA), high valence and low arousal (HVLA) and low valence high arousal (LVHA). In the proposed method, multimodal signals (EEG, ECG, and GSR) are preprocessed using bandpass filtering and independent components analysis (ICA) for noise removal in EEG signals followed by discrete wavelet transform for time domain to frequency domain conversion. Discrete wavelet transform results in spectrograms of the physiological signal and then features are extracted using stacked autoencoders from those spectrograms. A feature vector is obtained from the bottleneck layer of the autoencoder and is fed to three classifiers SVM (support vector machine), RF (random forest), and LSTM (long short-term memory) followed by majority voting as ensemble classification. The proposed system is trained and tested on the AMIGOS dataset with k-fold cross-validation. The proposed system obtained the highest accuracy of 94.5% and shows improved results of the proposed method compared with other state-of-the-art methods. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
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17 pages, 6844 KiB  
Article
Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis
by Mary Judith Antony, Baghavathi Priya Sankaralingam, Rakesh Kumar Mahendran, Akber Abid Gardezi, Muhammad Shafiq, Jin-Ghoo Choi and Habib Hamam
Sensors 2022, 22(19), 7596; https://doi.org/10.3390/s22197596 - 07 Oct 2022
Cited by 18 | Viewed by 2048
Abstract
An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronounced when CSP filtering is used. Furthermore, traditional CSP methods lack [...] Read more.
An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronounced when CSP filtering is used. Furthermore, traditional CSP methods lack frequency domain information and require many input channels. Therefore, to overcome this shortcoming, a feature extraction method based on Online Recursive Independent Component Analysis (ORICA)-CSP is proposed. For EEG-based brain—computer interfaces (BCIs), especially online and real-time BCIs, the most widely used classifiers used to be linear discriminant analysis (LDA) and support vector machines (SVM). Previous evaluations clearly show that SVMs generally outperform other classifiers in terms of performance. In this case, Adaptive Support Vector Machine (A-SVM) is used for classification together with the ORICA-CSP method. The results are promising, and the experiments are performed on EEG data of 4 classes’ motor images, namely Dataset 2a of BCI Competition IV. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
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15 pages, 2058 KiB  
Article
Cooperative Energy-Efficient Routing Protocol for Underwater Wireless Sensor Networks
by Irfan Ahmad, Taj Rahman, Asim Zeb, Inayat Khan, Mohamed Tahar Ben Othman and Habib Hamam
Sensors 2022, 22(18), 6945; https://doi.org/10.3390/s22186945 - 14 Sep 2022
Cited by 16 | Viewed by 2346
Abstract
Underwater wireless sensor networks (UWSNs) contain sensor nodes that sense the data and then transfer them to the sink node or base station. Sensor nodes are operationalized through limited-power batteries. Therefore, improvement in energy consumption becomes critical in UWSNs. Data forwarding through the [...] Read more.
Underwater wireless sensor networks (UWSNs) contain sensor nodes that sense the data and then transfer them to the sink node or base station. Sensor nodes are operationalized through limited-power batteries. Therefore, improvement in energy consumption becomes critical in UWSNs. Data forwarding through the nearest sensor node to the sink or base station reduces the network’s reliability and stability because it creates a hotspot and drains the energy early. In this paper, we propose the cooperative energy-efficient routing (CEER) protocol to increase the network lifetime and acquire a reliable network. We use the sink mobility scheme to reduce energy consumption by eliminating the hotspot issue. We have divided the area into multiple sections for better deployment and deployed the sink nodes in each area. Sensor nodes generate the data and send it to the sink nodes to reduce energy consumption. We have also used the cooperative technique to achieve reliability in the network. Based on simulation results, the proposed scheme performed better than existing routing protocols in terms of packet delivery ratio (PDR), energy consumption, transmission loss, and end-to-end delay. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
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21 pages, 2870 KiB  
Article
Comprehensive Analysis of Network Slicing for the Developing Commercial Needs and Networking Challenges
by Sumbal Zahoor, Ishtiaq Ahmad, Mohamed Tahar Ben Othman, Ali Mamoon, Ateeq Ur Rehman, Muhammad Shafiq and Habib Hamam
Sensors 2022, 22(17), 6623; https://doi.org/10.3390/s22176623 - 01 Sep 2022
Cited by 8 | Viewed by 2430
Abstract
Network slicing (NS) is one of the most prominent next-generation wireless cellular technology use cases, promising to unlock the core benefits of 5G network architecture by allowing communication service providers (CSPs) and operators to construct scalable and customized logical networks. This, in turn, [...] Read more.
Network slicing (NS) is one of the most prominent next-generation wireless cellular technology use cases, promising to unlock the core benefits of 5G network architecture by allowing communication service providers (CSPs) and operators to construct scalable and customized logical networks. This, in turn, enables telcos to reach the full potential of their infrastructure by offering customers tailored networking solutions that meet their specific needs, which is critical in an era where no two businesses have the same requirements. This article presents a commercial overview of NS, as well as the need for a slicing automation and orchestration framework. Furthermore, it will address the current NS project objectives along with the complex functional execution of NS code flow. A summary of activities in important standards development groups and industrial forums relevant to artificial intelligence (AI) and machine learning (ML) is also provided. Finally, we identify various open research problems and potential answers to provide future guidance. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
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21 pages, 10377 KiB  
Article
Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms
by Fatima Mahmood, Jehangir Arshad, Mohamed Tahar Ben Othman, Muhammad Faisal Hayat, Naeem Bhatti, Mujtaba Hussain Jaffery, Ateeq Ur Rehman and Habib Hamam
Sensors 2022, 22(17), 6389; https://doi.org/10.3390/s22176389 - 25 Aug 2022
Cited by 2 | Viewed by 3859
Abstract
Examination cheating activities like whispering, head movements, hand movements, or hand contact are extensively involved, and the rectitude and worthiness of fair and unbiased examination are prohibited by such cheating activities. The aim of this research is to develop a model to supervise [...] Read more.
Examination cheating activities like whispering, head movements, hand movements, or hand contact are extensively involved, and the rectitude and worthiness of fair and unbiased examination are prohibited by such cheating activities. The aim of this research is to develop a model to supervise or control unethical activities in real-time examinations. Exam supervision is fallible due to limited human abilities and capacity to handle students in examination centers, and these errors can be reduced with the help of the Automatic Invigilation System. This work presents an automated system for exams invigilation using deep learning approaches i.e., Faster Regional Convolution Neural Network (RCNN). Faster RCNN is an object detection algorithm that is implemented to detect the suspicious activities of students during examinations based on their head movements, and for student identification, MTCNN (Multi-task Cascaded Convolutional Neural Networks) is used for face detection and recognition. The training accuracy of the proposed model is 99.5% and the testing accuracy is 98.5%. The model is fully efficient in detecting and monitoring more than 100 students in one frame during examinations. Different real-time scenarios are considered to evaluate the performance of the Automatic Invigilation System. The proposed invigilation model can be implemented in colleges, universities, and schools to detect and monitor student suspicious activities. Hopefully, through the implementation of the proposed invigilation system, we can prevent and solve the problem of cheating because it is unethical. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
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16 pages, 3300 KiB  
Article
A Novel Decentralized Blockchain Architecture for the Preservation of Privacy and Data Security against Cyberattacks in Healthcare
by Ajitesh Kumar, Akhilesh Kumar Singh, Ijaz Ahmad, Pradeep Kumar Singh, Anushree, Pawan Kumar Verma, Khalid A. Alissa, Mohit Bajaj, Ateeq Ur Rehman and Elsayed Tag-Eldin
Sensors 2022, 22(15), 5921; https://doi.org/10.3390/s22155921 - 08 Aug 2022
Cited by 32 | Viewed by 3880
Abstract
Nowadays, in a world full of uncertainties and the threat of digital and cyber-attacks, blockchain technology is one of the major critical developments playing a vital role in the creative professional world. Along with energy, finance, governance, etc., the healthcare sector is one [...] Read more.
Nowadays, in a world full of uncertainties and the threat of digital and cyber-attacks, blockchain technology is one of the major critical developments playing a vital role in the creative professional world. Along with energy, finance, governance, etc., the healthcare sector is one of the most prominent areas where blockchain technology is being used. We all are aware that data constitute our wealth and our currency; vulnerability and security become even more significant and a vital point of concern for healthcare. Recent cyberattacks have raised the questions of planning, requirement, and implementation to develop more cyber-secure models. This paper is based on a blockchain that classifies network participants into clusters and preserves a single copy of the blockchain for every cluster. The paper introduces a novel blockchain mechanism for secure healthcare sector data management, which reduces the communicational and computational overhead costs compared to the existing bitcoin network and the lightweight blockchain architecture. The paper also discusses how the proposed design can be utilized to address the recognized threats. The experimental results show that, as the number of nodes rises, the suggested architecture speeds up ledger updates by 63% and reduces network traffic by 10 times. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
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14 pages, 1747 KiB  
Article
Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data
by Nadia Muhammad Hussain, Ateeq Ur Rehman, Mohamed Tahar Ben Othman, Junaid Zafar, Haroon Zafar and Habib Hamam
Sensors 2022, 22(14), 5103; https://doi.org/10.3390/s22145103 - 07 Jul 2022
Cited by 19 | Viewed by 2587
Abstract
Artificial intelligence is serving as an impetus in digital health, clinical support, and health informatics for an informed patient’s outcome. Previous studies only consider classification accuracies of cardiotocographic (CTG) datasets and disregard computational time, which is a relevant parameter in a clinical environment. [...] Read more.
Artificial intelligence is serving as an impetus in digital health, clinical support, and health informatics for an informed patient’s outcome. Previous studies only consider classification accuracies of cardiotocographic (CTG) datasets and disregard computational time, which is a relevant parameter in a clinical environment. This paper proposes a modified deep neural algorithm to classify untapped pathological and suspicious CTG recordings with the desired time complexity. In our newly developed classification algorithm, AlexNet architecture is merged with support vector machines (SVMs) at the fully connected layers to reduce time complexity. We used an open-source UCI (Machine Learning Repository) dataset of cardiotocographic (CTG) recordings. We divided 2126 CTG recordings into 3 classes (Normal, Pathological, and Suspected), including 23 attributes that were dynamically programmed and fed to our algorithm. We employed a deep transfer learning (TL) mechanism to transfer prelearned features to our model. To reduce time complexity, we implemented a strategy wherein layers in the convolutional base were partially trained to leave others in the frozen states. We used an ADAM optimizer for the optimization of hyperparameters. The presented algorithm also outperforms the leading architectures (RCNNs, ResNet, DenseNet, and GoogleNet) with respect to real-time accuracies, sensitivities, and specificities of 99.72%, 96.67%, and 99.6%, respectively, making it a viable candidate for clinical settings after real-time validation. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
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18 pages, 2004 KiB  
Article
Early-Stage Alzheimer’s Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains
by Ahsan Bin Tufail, Nazish Anwar, Mohamed Tahar Ben Othman, Inam Ullah, Rehan Ali Khan, Yong-Kui Ma, Deepak Adhikari, Ateeq Ur Rehman, Muhammad Shafiq and Habib Hamam
Sensors 2022, 22(12), 4609; https://doi.org/10.3390/s22124609 - 18 Jun 2022
Cited by 18 | Viewed by 2488
Abstract
Alzheimer’s Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and amyloid plaques in the early as well as later [...] Read more.
Alzheimer’s Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and amyloid plaques in the early as well as later stages of the disease. Neuroimaging modalities are routinely used in clinical practice to capture brain alterations associated with AD. On the other hand, deep learning methods are routinely used to recognize patterns in underlying data distributions effectively. This work uses Convolutional Neural Network (CNN) architectures in both 2D and 3D domains to classify the initial stages of AD into AD, Mild Cognitive Impairment (MCI) and Normal Control (NC) classes using the positron emission tomography neuroimaging modality deploying data augmentation in a random zoomed in/out scheme. We used novel concepts such as the blurring before subsampling principle and distant domain transfer learning to build 2D CNN architectures. We performed three binaries, that is, AD/NC, AD/MCI, MCI/NC and one multiclass classification task AD/NC/MCI. The statistical comparison revealed that 3D-CNN architecture performed the best achieving an accuracy of 89.21% on AD/NC, 71.70% on AD/MCI, 62.25% on NC/MCI and 59.73% on AD/NC/MCI classification tasks using a five-fold cross-validation hyperparameter selection approach. Data augmentation helps in achieving superior performance on the multiclass classification task. The obtained results support the application of deep learning models towards early recognition of AD. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
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20 pages, 5178 KiB  
Article
Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier
by Rohit Srivastava, Ved Prakash Bhardwaj, Mohamed Tahar Ben Othman, Mukesh Pushkarna, Anushree, Arushi Mangla, Mohit Bajaj, Ateeq Ur Rehman, Muhammad Shafiq and Habib Hamam
Sensors 2022, 22(10), 3620; https://doi.org/10.3390/s22103620 - 10 May 2022
Cited by 7 | Viewed by 1999
Abstract
Biometrics is the term for measuring human characteristics. If the term is divided into two parts, bio means life, and metric means measurement. The measurement of humans through different computational methods is performed to authorize a person. This measurement can be performed via [...] Read more.
Biometrics is the term for measuring human characteristics. If the term is divided into two parts, bio means life, and metric means measurement. The measurement of humans through different computational methods is performed to authorize a person. This measurement can be performed via a single biometric or by using a combination of different biometric traits. The combination of multiple biometrics is termed biometric fusion. It provides a reliable and secure authentication of a person at a higher accuracy. It has been introduced in the UIDIA framework in India (AADHAR: Association for Development and Health Action in Rural) and in different nations to figure out which biometric characteristics are suitable enough to authenticate the human identity. Fusion in biometric frameworks, especially FKP (finger–knuckle print) and iris, demonstrated to be a solid multimodal as a secure framework. The proposed approach demonstrates a proficient and strong multimodal biometric framework that utilizes FKP and iris as biometric modalities for authentication, utilizing scale-invariant feature transform (SIFT) and speeded up robust features (SURF). Log Gabor wavelet is utilized to extricate the iris feature set. From the extracted region, features are computed using principal component analysis (PCA). Both biometric modalities, FKP and iris, are combined at the match score level. The matching is performed using a neuro-fuzzy neural network classifier. The execution and accuracy of the proposed framework are tested on the open database Poly-U, CASIA, and an accuracy of 99.68% is achieved. The accuracy is higher compared to a single biometric. The neuro-fuzzy approach is also tested in comparison to other classifiers, and the accuracy is 98%. Therefore, the fusion mechanism implemented using a neuro-fuzzy classifier provides the best accuracy compared to other classifiers. The framework is implemented in MATLAB 7.10. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
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Review

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27 pages, 6909 KiB  
Review
Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects
by Mahmoud Salem, Ahmed Elkaseer, Islam A. M. El-Maddah, Khaled Y. Youssef, Steffen G. Scholz and Hoda K. Mohamed
Sensors 2022, 22(17), 6625; https://doi.org/10.3390/s22176625 - 01 Sep 2022
Cited by 7 | Viewed by 3724
Abstract
The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and [...] Read more.
The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system. Full article
(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)
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