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The Future Internet of Medical Things

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A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (15 September 2021) | Viewed by 36055

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Special Issue Editor

Dr. Matthew Pediaditis

E-Mail Website1 Website2
Guest Editor

Foundation for Research and Technology-Hellas, Heraklion, Greece
Interests: AI in healthcare; deep learning for patient monitoring; wearable devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Medical Things (IoMT) is often declared as the future of healthcare. It is an amalgamation of medical devices and applications that are connected to health information technology systems using state-of-the-art communication technologies.

IoMT consists of smart devices, such as wearables and medical monitors, designed for healthcare purposes to be used on the human body, at home, or in the community as well as in clinical settings. The on-body segment can be divided into consumer health wearables and medical wearables. At home, we find personal emergency response systems, remote patient monitoring, and virtual doctors. In the community are mobility services for health tracking during transit, emergency response intelligence, point-of-care devices, and logistics involving the transport and delivery of healthcare goods and services. Finally, in the clinical domain, IoMT is used for clinical (nursing, smart health monitoring, and more) and administrative functions (asset and personnel management, patient flow management, inventory management, etc.)

Moreover, IoMT includes cloud-based platforms hosting applications that will bring together data from all aforementioned devices that have been obscured from each other throughout the healthcare industry so far. In doing so, IoMT will create useful data to bring healthcare insights and professionals together in order to advance medicine and human health.

The aim of this Special Issue is to highlight the most recent innovations in IoMT technologies able to provide secure, unobtrusive, and continuous health monitoring and analytic services.

The topics of this Special Issue include, but are not limited to:

State-of-the-art IoMT health monitoring and sensing paradigms;
Data collection, integration, and interpretation in IoMT;
Unobtrusive health monitoring with IoMT;
IoMT security solutions regarding sensitive medical data;
IoMT network architectures and connectivity;
Edge computing in the domain of IoMT;
Ultra-low-power IoMT devices;
IoMT and Healthcare 4.0.

Dr. Matthew Pediaditis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly 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 1600 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.

Published Papers (7 papers)

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Research

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23 pages, 2139 KiB

Open AccessArticle

CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario

by Grazia Veronica Aiosa, Barbara Attanasio, Aurelio La Corte and Marialisa Scatá

Future Internet 2021, 13(7), 177; https://doi.org/10.3390/fi13070177 - 7 Jul 2021

Cited by 3 | Viewed by 2408

Abstract

The forthcoming 6G will attempt to rewrite the communication networks’ perspective focusing on a shift in paradigm in the way technologies and services are conceived, integrated and used. In this viewpoint, the Internet of Medical Things (IoMT) represents a merger of medical devices and health applications that are connected through networks, introducing an important change in managing the disease, treatments and diagnosis, reducing costs and faults. In 6G, the edge intelligence moves the innovative abilities from the central cloud to the edge and jointly with the complex systems approach will enable the development of a new category of lightweight applications as microservices. It requires edge intelligence also for the service evaluation in order to introduce the same degree of adaptability. We propose a new evaluation model, called CoKnowEMe (context knowledge evaluation model), by introducing an architectural and analytical scheme, modeled following a complex and dynamical approach, consisting of three inter-operable level and different networked attributes, to quantify the quality of IoMT microservices depending on a changeable context of use. We conduct simulations to display and quantify the structural complex properties and performance statistical estimators. We select and classify suitable attributes through a further detailed procedure in a supplementary information document. Full article

(This article belongs to the Special Issue The Future Internet of Medical Things)

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18 pages, 34247 KiB

Open AccessArticle

Telehealth Using PoseNet-Based System for In-Home Rehabilitation

by Jiaming Chua, Lee-Yeng Ong and Meng-Chew Leow

Future Internet 2021, 13(7), 173; https://doi.org/10.3390/fi13070173 - 2 Jul 2021

Cited by 14 | Viewed by 4197

Abstract

The increasing cost of healthcare services is accelerating the development of the telehealth system to fulfill the necessity of delivering an efficient and cost-effective remote healthcare services. Moreover, the ageing of the global population and the disruption of the COVID-19 pandemic are creating a rapid rise of demand for healthcare services. This includes those who are in need of remote monitoring for chronic conditions through rehabilitation exercises. Therefore, this paper presents a telehealth system using PoseNet for in-home rehabilitation, with built-in statistical computation for doctors to analyze the patient’s recovery status. This system enables patients to perform rehabilitation exercises at home using an ordinary webcam. The PoseNet skeleton-tracking method is applied to detect and track the patients’ angular movements for both elbows and knees. By using this system, the measurement of the elbow and knee joint angles can be calculated and recorded while patients are performing rehabilitation exercises in front of the laptop webcam. After the patients complete their rehabilitation exercises, the skeleton results of four body parts will be generated. Based on the same actions performed by patients on selected days, the doctors can examine and evaluate the deviation rate of patients’ angular movements between different days to determine the recovery rate. Full article

(This article belongs to the Special Issue The Future Internet of Medical Things)

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19 pages, 3738 KiB

Open AccessArticle

User Acceptance of Smart Watch for Medical Purposes: An Empirical Study

by Rana Saeed Al-Maroof, Khadija Alhumaid, Ahmad Qasim Alhamad, Ahmad Aburayya and Said Salloum

Future Internet 2021, 13(5), 127; https://doi.org/10.3390/fi13050127 - 12 May 2021

Cited by 29 | Viewed by 7503

Abstract

This study aims to investigate the most effective and interesting variables that urge use of the smartwatch (SW) in a medical environment. To achieve this aim, the study was framed using an innovative and integrated research model, which is based on combining constructs from a well-established theoretical model’s TAM and other features that are critical to the effectiveness of SW which are content richness and personal innovativeness. The Technology Acceptance Model (TAM) is used to detect the determinants affecting the adoption of SW. The current study depends on an online questionnaire that is composed of (20) items. The questionnaire is distributed among a group of doctors, nurses, and administration staff in medical centers within the UAE. The total number of respondents is (325). The collected data were implemented to test the study model and the proposed constructs and hypotheses depending on the Smart PLS Software. The results of the current study show that the main constructs in the model contribute differently to the acceptance of SW. Based on the previous assumption, content richness and innovativeness are critical factors that enrich the user’s perceived usefulness. In addition, perceived ease of use was significantly predictive of either perceived usefulness or behavioral intention. Overall findings suggest that SW is in high demand in the medical field and is used as a common channel among doctors and their patients and it facilitates the role of transmitting information among its users. The outcomes of the current study indicate the importance of certain external factors for the acceptance of the technology. The genuine value of this study lies in the fact that it is based on a conceptual framework that emphasizes the close relationship between the TAM constructs of perceived usefulness and perceived ease of use to the construct of content richness, and innovativeness. Finally, this study helps us recognize the embedded motives for using SW in a medical environment, where the main motive is to enhance and facilitate the effective roles of doctors and patients. Full article

(This article belongs to the Special Issue The Future Internet of Medical Things)

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14 pages, 1365 KiB

Open AccessArticle

Deep Learning-Based Classification of Fine Hand Movements from Low Frequency EEG

by Giulia Bressan, Giulia Cisotto, Gernot R. Müller-Putz and Selina Christin Wriessnegger

Future Internet 2021, 13(5), 103; https://doi.org/10.3390/fi13050103 - 21 Apr 2021

Cited by 28 | Viewed by 3561

Abstract

The classification of different fine hand movements from electroencephalogram (EEG) signals represents a relevant research challenge, e.g., in BCI applications for motor rehabilitation. Here, we analyzed two different datasets where fine hand movements (touch, grasp, palmar, and lateral grasp) were performed in a self-paced modality. We trained and tested a newly proposed CNN, and we compared its classification performance with two well-established machine learning models, namely, shrinkage-linear discriminant analysis (LDA) and Random Forest (RF). Compared to previous literature, we included neuroscientific evidence, and we trained our Convolutional Neural Network (CNN) model on the so-called movement-related cortical potentials (MRCPs). They are EEG amplitude modulations at low frequencies, i.e.,

(0.3, 3)

Hz that have been proved to encode several properties of the movements, e.g., type of grasp, force level, and speed. We showed that CNN achieved good performance in both datasets (accuracy of

0.70 \pm 0.11

and

0.64 \pm 0.10

, for the two datasets, respectively), and they were similar or superior to the baseline models (accuracy of

0.68 \pm 0.10

and

0.62 \pm 0.07

with sLDA; accuracy of

0.70 \pm 0.15

and

0.61 \pm 0.07

with RF, with comparable performance in precision and recall). In addition, compared to the baseline, our CNN requires a faster pre-processing procedure, paving the way for its possible use in online BCI applications. Full article

(This article belongs to the Special Issue The Future Internet of Medical Things)

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24 pages, 3974 KiB

Open AccessFeature PaperArticle

An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care

by Rui Hu, Bruno Michel, Dario Russo, Niccolò Mora, Guido Matrella, Paolo Ciampolini, Francesca Cocchi, Enrico Montanari, Stefano Nunziata and Thomas Brunschwiler

Future Internet 2021, 13(1), 6; https://doi.org/10.3390/fi13010006 - 30 Dec 2020

Cited by 20 | Viewed by 4895

Abstract

Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects’ health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia–Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects’ daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient’s behavior as a ‘Bag of Words’, based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects’ daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort. Full article

(This article belongs to the Special Issue The Future Internet of Medical Things)

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23 pages, 5216 KiB

Open AccessArticle

Research Trend on the Use of IT in Digital Addiction: An Investigation Using a Systematic Literature Review

by Flourensia Sapty Rahayu, Lukito Edi Nugroho, Ridi Ferdiana and Djoko Budiyanto Setyohadi

Future Internet 2020, 12(10), 174; https://doi.org/10.3390/fi12100174 - 18 Oct 2020

Cited by 4 | Viewed by 4718

Abstract

Despite the negative role of IT in digital addiction development, IT may have a positive role in dealing with digital addiction. The present study undertakes a systematic literature review to explore the state of play and the trend regarding the use of IT in digital addiction research. Using predefined keywords, the Scopus database was searched for relevant literature published from 2017 to 2020. The initial search found 1655 papers. Six stages of study selection were completed using a set of inclusion and exclusion criteria. The study selection and quality assessment process were applied, then 15 papers were selected for further review. The results show that addiction detection using IT is the most researched topic in digital addiction research. The most commonly used IT in the selected studies are AI methods and biosignal recording systems. Various approaches in detection, prevention, and intervention are suggested in the selected studies. The advantages and limitations of each approach are discussed. Based on these results, some future research directions are suggested. Full article

(This article belongs to the Special Issue The Future Internet of Medical Things)

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Review

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16 pages, 364 KiB

Open AccessReview

A Review on Blockchain for the Internet of Medical Things: Definitions, Challenges, Applications, and Vision

by Gioele Bigini, Valerio Freschi and Emanuele Lattanzi

Future Internet 2020, 12(12), 208; https://doi.org/10.3390/fi12120208 - 25 Nov 2020

Cited by 35 | Viewed by 6011

Abstract

Nowadays, there are a lot of new mobile devices that have the potential to assist healthcare professionals when working and help to increase the well-being of the people. These devices comprise the Internet of Medical Things, but it is generally difficult for healthcare institutions to meet compliance of their systems with new medical solutions efficiently. A technology that promises the sharing of data in a trust-less scenario is the Distributed Ledger Technology through its properties of decentralization, immutability, and transparency. The Blockchain and the Internet of Medical Things can be considered as at an early stage, and the implementations successfully applying the technology are not so many. Some aspects covered by these implementations are data sharing, interoperability of systems, security of devices, the opportunity of data monetization and data ownership that will be the focus of this review. This work aims at giving an overview of the current state-of-the-art of the Blockchain-based systems for the Internet of Medical Things, specifically addressing the challenges of reaching user-centricity for these combined systems, and thus highlighting the potential future directions to follow for full ownership of data by users. Full article

(This article belongs to the Special Issue The Future Internet of Medical Things)

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