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Ambient Assistive Methodologies/Frameworks for Internet of Medical Things

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 68988

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Special Issue Editors

Dr. Hemant Ghayvat

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Guest Editor

Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Växjö, Sweden
Interests: assistive technology; diagnostic image processing; acoustics singal processing; human-computer interaction; preventive healthcare monitoring and diagnose; lifestyle pattern modeling; machine learning

Dr. Sharnil Pandya

E-Mail Website
Guest Editor

Department of Computer Science, Symbiosis Institute of Technology, Symbiosis International University, Pune, India
Interests: smart sensing; health informatics; acoustics health; block chaining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent times, hospital-centric healthcare services have shifted to home-centric healthcare services using Internet of Things (IoT)-enabled wearable devices and wireless acoustic solutions. Due to a paradigm shift in technologies such as the IoT and AI-enabled wearable devices, the healthcare discipline has drastically transformed. However, most recent research has been aimed at specific sensors, methodologies, and healthcare applications. Although IoT-enabled heterogeneous sensors can provide remote healthcare monitoring solutions, the IoT and explainable AI-based preventive healthcare frameworks have remained open research issues for fellow researchers.

With the increasing demand for lower-cost, sophisticated healthcare tools, and solutions, there is an immediate need to propose a complete ambient assistive framework for providing 24 x 7 connectivity among smart devices, a healthcare cloud, and health web and mobile interfaces. In the IoMT, enabling technologies include intelligent biosensors and bioelectronics, wearable healthcare gadgets, big-data-based health frameworks, and rigorous health diagnosis.

This Special Issue focuses on state-of-the-art fog and edge computing-based ambient assistive healthcare frameworks, tools, and system submissions. It also encourages acoustic and computer-vision-based healthcare solution-related articles. This issue aims to promote awareness of ambient healthcare systems, fog and edge computing-based healthcare services, and emergency healthcare solutions.

Wearable healthcare solutions embedded with biosensors;
Ambient assistive frameworks and systems;
Ambient healthcare frameworks such as HL 7;
Ambient healthcare systems and point-of-care healthcare instrumentation;
Ubiquitous healthcare systems and architectures;
Sensor-fusion based ambient healthcare solutions;
Bioinformatics for healthcare engineering and systems;
Wireless acoustic systems for healthcare;
Medical data mining and big data analytics;
Innovative acoustic classification frameworks and methodologies;
Biomedical signal and image processing of IoMT;
IoMT architecture, implementation, and application.

Dr. Hemant Ghayvat
Dr. Sharnil Pandya
Guest Editors

Manuscript Submission Information

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Keywords

Internet of Medical Things
Internet of Things
Ambient intelligence
Ambient healthcare frameworks
Big data
Sensor fusion
Data fusing
Ubiquitous healthcare systems
Fog computing
Edge computing
Cloud computing
Healthcare services
Acoustic classification

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Published Papers (8 papers)

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Research

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36 pages, 8553 KiB

Open AccessArticle

Identification of Secondary Breast Cancer in Vital Organs through the Integration of Machine Learning and Microarrays

by Faisal Riaz, Fazeel Abid, Ikram Ud Din, Byung-Seo Kim, Ahmad Almogren and Shajara Ul Durar

Electronics 2022, 11(12), 1879; https://doi.org/10.3390/electronics11121879 - 15 Jun 2022

Cited by 1 | Viewed by 2116

Abstract

Breast cancer includes genetic and environmental factors and is the most prevalent malignancy in women contributing to the pathogenesis and progression of cancer. Breast cancer prognosis metastasizes towards bones, the liver, brain, and lungs, and is the main cause of death in patients. Furthermore, the selection of features and classification is significant in microarray data analysis, which suffers from huge time consumption. To address these issues, this research uniquely integrates machine learning and microarrays to identify secondary breast cancer in vital organs. This work firstly imputes the missing values using K-nearest neighbors and improves the recursive feature elimination with cross-validation (RFECV) using the random forest method. Secondly, the class imbalance is handled by employing K-means synthetic object oversampling technique (SMOTE) to balance minority class and prevent noise. We successfully identified the 16 most essential Entrez gene ids responsible for predicting metastatic locations in the bones, brain, liver, and lungs. Extensive experiments are conducted on NCBI Gene Expression Omnibus GSE14020 and GSE54323 datasets. The proposed methods have handled class imbalance, prevented noise, and appropriately reduced time consumption. Reliable results were obtained on four classification models: decision tree; K-nearest neighbors; random forest; and support vector machine. Results are presented having considered confusion matrices, accuracy, ROC-AUC and PR-AUC, and F1-score. Full article

(This article belongs to the Special Issue Ambient Assistive Methodologies/Frameworks for Internet of Medical Things)

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13 pages, 489 KiB

Open AccessArticle

Deep Learning for Depression Detection from Textual Data

by Amna Amanat, Muhammad Rizwan, Abdul Rehman Javed, Maha Abdelhaq, Raed Alsaqour, Sharnil Pandya and Mueen Uddin

Electronics 2022, 11(5), 676; https://doi.org/10.3390/electronics11050676 - 23 Feb 2022

Cited by 124 | Viewed by 15965

Abstract

Depression is a prevalent sickness, spreading worldwide with potentially serious implications. Timely recognition of emotional responses plays a pivotal function at present, with the profound expansion of social media and users of the internet. Mental illnesses are highly hazardous, stirring more than three hundred million people. Moreover, that is why research is focused on this subject. With the advancements of machine learning and the availability of sample data relevant to depression, there is the possibility of developing an early depression diagnostic system, which is key to lessening the number of afflicted individuals. This paper proposes a productive model by implementing the Long-Short Term Memory (LSTM) model, consisting of two hidden layers and large bias with Recurrent Neural Network (RNN) with two dense layers, to predict depression from text, which can be beneficial in protecting individuals from mental disorders and suicidal affairs. We train RNN on textual data to identify depression from text, semantics, and written content. The proposed framework achieves 99.0% accuracy, higher than its counterpart, frequency-based deep learning models, whereas the false positive rate is reduced. We also compare the proposed model with other models regarding its mean accuracy. The proposed approach indicates the feasibility of RNN and LSTM by achieving exceptional results for early recognition of depression in the emotions of numerous social media subscribers. Full article

(This article belongs to the Special Issue Ambient Assistive Methodologies/Frameworks for Internet of Medical Things)

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14 pages, 1419 KiB

Open AccessArticle

The Estimation of the Potential for Using Smart-Trackers as a Part of a Medical Indoor-Positioning System

by Irina V. Pospelova, Irina V. Cherepanova, Dmitry S. Bragin, Ivan A. Sidorov, Evgeny Y. Kostyuchenko and Victoriya N. Serebryakova

Electronics 2022, 11(1), 107; https://doi.org/10.3390/electronics11010107 - 29 Dec 2021

Cited by 3 | Viewed by 2224

Abstract

This research aims to estimate the feasibility of using smart-bracelets as a part of a medicine indoor-positioning system, to monitor the health status and location of patients in a hospital. The smart-bracelet takes on the role of a token of the system and can measure pulse, blood pressure and saturation and provide data transmission over the BLE. The distance between token and anchor was calculated by the RSSI. The position of a token and anchor relative to each other was determined by the trilateration method. The results of the research showed that the accuracy of the developed system in a static position is 1.46 m and exceeds 3 m in a dynamic position. Results of experiments showed that measurements from the smart bracelets are transmitted to the server of the system without distortion. The study results indicated that smart-bracelets could be used to locate patients inside a hospital or estimate their current health state. Given the low accuracy of systolic pressure measurement, it is recommended to develop an algorithm that will allow smooth measuring error for higher-precision estimation of the patient’s general health state. In addition, it is planned to improve the positioning algorithm. Full article

(This article belongs to the Special Issue Ambient Assistive Methodologies/Frameworks for Internet of Medical Things)

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11 pages, 257 KiB

Open AccessArticle

Public Needs for Wearable Particulate Matter Devices and Their Influencing Factors

by Haiying Wang, Lin Wang, Heechan Kang, Moon-Hyon Hwang, Do Gyun Lee and Da Young Ju

Electronics 2021, 10(24), 3069; https://doi.org/10.3390/electronics10243069 - 9 Dec 2021

Cited by 2 | Viewed by 2314

Abstract

Recently, increasing numbers of people have realized the harm that particulate matter (PM) causes to health, especially those with a diameter less than 2.5 μm (PM2.5). With the increasing popularity of wearable devices in recent years, it is believed that wearable technology can contribute feasible solutions to prevent health hazards caused by PM2.5. In order to better understand the public’s needs regarding wearable devices, this study aimed to determine what kinds of PM2.5 wearable devices were needed by the public and the factors that may influence these needs. An online survey was conducted in the Beijing metropolitan area of China of a total of 894 subjects. The results showed that the public’s overall need for wearable PM2.5 purifiers was higher than for wearable PM2.5 trackers. The public’s needs for wearable breathing-zone PM2.5 devices were significantly higher than for any other type, indicating that people care about the quality of the air they actually breathe. It was also found that education, income level, and attitude toward PM2.5 positively affected their needs for wearable devices. In contrast, age had a negative influence on their needs. The results of this study are expected to serve as a valuable reference for related academic and industrial research. Full article

(This article belongs to the Special Issue Ambient Assistive Methodologies/Frameworks for Internet of Medical Things)

12 pages, 1921 KiB

Open AccessFeature PaperArticle

Functional Connectivity of EEG in Encephalitis during Slow Biphasic Complexes

by Giovanni Chiarion and Luca Mesin

Electronics 2021, 10(23), 2978; https://doi.org/10.3390/electronics10232978 - 30 Nov 2021

Cited by 6 | Viewed by 1742

Abstract

The electroencephalogram (EEG) of patients suffering from inflammatory diseases of the brain may show specific waveforms called slow biphasic complexes (SBC). Recent studies indicated a correlation between the severity of encephalitis and some features of SBCs, such as location, amplitude and frequency of appearance. Moreover, EEG rhythms were found to vary before the onset of an SBC, as if the brain was preparing to the discharge (actually with a slowing down of the EEG oscillation). Here, we investigate possible variations of EEG functional connectivity (FC) in EEGs from pediatric patients with different levels of severity of encephalitis. FC was measured by the maximal crosscorrelation of EEG rhythms in different bipolar channels. Then, the indexes of network patterns (namely strength, clustering coefficient, efficiency and characteristic path length) were estimated to characterize the global behavior when they are measured during SBCs or far from them. EEG traces showed statistical differences in the two conditions: clustering coefficient, efficiency and strength are higher close to an SBC, whereas the characteristic path length is lower. Moreover, for more severe conditions, an increase in clustering coefficient, efficiency and strength and a decrease in characteristic path length were observed in the delta–theta band. These outcomes support the hypothesis that SBCs result from the anomalous coordination of neurons in different brain areas affected by the inflammation process and indicate FC as an additional key for interpreting the EEG in encephalitis patients. Full article

(This article belongs to the Special Issue Ambient Assistive Methodologies/Frameworks for Internet of Medical Things)

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21 pages, 2842 KiB

Open AccessArticle

Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness

by Rupali Gangarde, Amit Sharma, Ambika Pawar, Rahul Joshi and Sudhanshu Gonge

Electronics 2021, 10(22), 2877; https://doi.org/10.3390/electronics10222877 - 22 Nov 2021

Cited by 22 | Viewed by 3394

Abstract

As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of the COVID-19 pandemic. Today, OSNs have become a core part of many people’s daily lifestyles. Therefore, increasing dependency on OSNs encourages privacy requirements to protect users from malicious sources. OSNs contain sensitive information about each end user that intruders may try to leak for commercial or non-commercial purposes. Therefore, ensuring different levels of privacy is a vital requirement for OSNs. Various privacy preservation methods have been introduced recently at the user and network levels, but ensuring k-anonymity and higher privacy model requirements such as l-diversity and t-closeness in OSNs is still a research challenge. This study proposes a novel method that effectively anonymizes OSNs using multiple-graph-properties-based clustering. The clustering method introduces the goal of achieving privacy of edge, node, and user attributes in the OSN graph. This clustering approach proposes to ensure k-anonymity, l-diversity, and t-closeness in each cluster of the proposed model. We first design the data normalization algorithm to preprocess and enhance the quality of raw OSN data. Then, we divide the OSN data into different clusters using multiple graph properties to satisfy the k-anonymization. Furthermore, the clusters ensure improved k-anonymization by a novel one-pass anonymization algorithm to address l-diversity and t-closeness privacy requirements. We evaluate the performance of the proposed method with state-of-the-art methods using a “Yelp real-world dataset”. The proposed method ensures high-level privacy preservation compared to state-of-the-art methods using privacy metrics such as anonymization degree, information loss, and execution time. Full article

(This article belongs to the Special Issue Ambient Assistive Methodologies/Frameworks for Internet of Medical Things)

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Review

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23 pages, 828 KiB

Open AccessReview

A Review on Deep Learning Techniques for IoT Data

by Kuruva Lakshmanna, Rajesh Kaluri, Nagaraja Gundluru, Zamil S. Alzamil, Dharmendra Singh Rajput, Arfat Ahmad Khan, Mohd Anul Haq and Ahmed Alhussen

Electronics 2022, 11(10), 1604; https://doi.org/10.3390/electronics11101604 - 18 May 2022

Cited by 88 | Viewed by 8644

Abstract

Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments. Full article

(This article belongs to the Special Issue Ambient Assistive Methodologies/Frameworks for Internet of Medical Things)

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28 pages, 4424 KiB

Open AccessReview

CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope

by Dulari Bhatt, Chirag Patel, Hardik Talsania, Jigar Patel, Rasmika Vaghela, Sharnil Pandya, Kirit Modi and Hemant Ghayvat

Electronics 2021, 10(20), 2470; https://doi.org/10.3390/electronics10202470 - 11 Oct 2021

Cited by 412 | Viewed by 30757

Abstract

Computer vision is becoming an increasingly trendy word in the area of image processing. With the emergence of computer vision applications, there is a significant demand to recognize objects automatically. Deep CNN (convolution neural network) has benefited the computer vision community by producing excellent results in video processing, object recognition, picture classification and segmentation, natural language processing, speech recognition, and many other fields. Furthermore, the introduction of large amounts of data and readily available hardware has opened new avenues for CNN study. Several inspirational concepts for the progress of CNN have been investigated, including alternative activation functions, regularization, parameter optimization, and architectural advances. Furthermore, achieving innovations in architecture results in a tremendous enhancement in the capacity of the deep CNN. Significant emphasis has been given to leveraging channel and spatial information, with a depth of architecture and information processing via multi-path. This survey paper focuses mainly on the primary taxonomy and newly released deep CNN architectures, and it divides numerous recent developments in CNN architectures into eight groups. Spatial exploitation, multi-path, depth, breadth, dimension, channel boosting, feature-map exploitation, and attention-based CNN are the eight categories. The main contribution of this manuscript is in comparing various architectural evolutions in CNN by its architectural change, strengths, and weaknesses. Besides, it also includes an explanation of the CNN’s components, the strengths and weaknesses of various CNN variants, research gap or open challenges, CNN applications, and the future research direction. Full article

(This article belongs to the Special Issue Ambient Assistive Methodologies/Frameworks for Internet of Medical Things)

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