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New Trends in Intelligent Social-Health Systems Empowered by Internet of Every Things

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 30878

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Special Issue Editors

Dr. Macarena Espinilla-Estevéz

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Guest Editor

Departament of Computer Science, University of Jaén, 23071 Jaén, Spain
Interests: smart environments; e-Health; intelligent systems; soft computing
Special Issues, Collections and Topics in MDPI journals

Prof.Dr. Mª Ángeles Verdejo-Espinosa

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Guest Editor

Universidad de Jaen, Jaen, Spain
Interests: Smart Grids; Smart Cities; Accessibility in engineering

Dr. Francisco Javier Cabrerizo-Lorite

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Guest Editor

Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, DaSCI, University of Granada, 18071 Granada, Spain
Interests: intelligent decision making; artificial intelligence; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Prof.Dr. Yolanda María de la Fuente Robles

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Guest Editor

University of Jaén, 23071 Jaén, Spain
Interests: Dependency; Disability; Aging and Accessibility

Special Issue Information

The population pyramid is changing at an increasingly rapid pace, and the new structure involves unavoidable changes in public services and their financing, with particular reference to the world of health and social services. There is a real need to incorporate artificial intelligence into their processes over the next few years with intelligent systems that learn from existing applications in order to process and manipulate data and communicate with other expert systems. In this context, the Internet of Everything and devices with sensors play a key role in providing comfort, greater productivity, and cost reduction for individuals and companies.

This Special Issue is addressed to all intelligent social health systems designed with sensors following the Internet-of-Everything paradigm.

Dr. Macarena Espinilla-Estevéz
Prof. Dr. Mª Ángeles Verdejo-Espinosa
Dr. Francisco Javier Cabrerizo-Lorite
Prof. Dr. Yolanda María de la Fuente Robles
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

intelligent systems
Internet of Everything
artificial intelligence
soft computing
social health systems
accessibility
dependency
disability
aging
accessibility

Published Papers (5 papers)

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Research

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24 pages, 3379 KiB

Open AccessArticle

Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management

by Wei-Ting Hsiao, Yao-Chiang Kan, Chin-Chi Kuo, Yu-Chieh Kuo, Sin-Kuo Chai and Hsueh-Chun Lin

Sensors 2022, 22(2), 689; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020689 - 17 Jan 2022

Cited by 3 | Viewed by 2324

Abstract

We established a web-based ubiquitous health management (UHM) system, “ECG4UHM”, for processing ECG signals with AI-enabled models to recognize hybrid arrhythmia patterns, including atrial premature atrial complex (APC), atrial fibrillation (AFib), ventricular premature complex (VPC), and ventricular tachycardia (VT), versus normal sinus rhythm (NSR). The analytical model coupled machine learning methods, such as multiple layer perceptron (MLP), random forest (RF), support vector machine (SVM), and naive Bayes (NB), to process the hybrid patterns of four arrhythmia symptoms for AI computation. The data pre-processing used Hilbert–Huang transform (HHT) with empirical mode decomposition to calculate ECGs’ intrinsic mode functions (IMFs). The area centroids of the IMFs’ marginal Hilbert spectrum were suggested as the HHT-based features. We engaged the MATLAB^TM compiler and runtime server in the ECG4UHM to build the recognition modules for driving AI computation to identify the arrhythmia symptoms. The modeling extracted the crucial data sets from the MIT-BIH arrhythmia open database. The validated models, including the premature pattern (i.e., APC–VPC) and the fibril-rapid pattern (i.e., AFib–VT) against NSR, could reach the best area under the curve (AUC) of the receiver operating characteristic (ROC) of approximately 0.99. The models for all hybrid patterns, without VPC versus AFib and VT, achieved an average accuracy of approximately 90%. With the prediction test, the respective AUCs of the NSR and APC versus the AFib, VPC, and VT were 0.94 and 0.93 for the RF and SVM on average. The average accuracy and the AUC of the MLP, RF, and SVM models for APC–VT reached the value of 0.98. The self-developed system with AI computation modeling can be the backend of the intelligent social-health system that can recognize hybrid arrhythmia patterns in the UHM and home-isolated cares. Full article

(This article belongs to the Special Issue New Trends in Intelligent Social-Health Systems Empowered by Internet of Every Things)

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32 pages, 12792 KiB

Open AccessArticle

DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

by Marcos Lupión, Javier Medina-Quero, Juan F. Sanjuan and Pilar M. Ortigosa

Sensors 2021, 21(2), 405; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020405 - 08 Jan 2021

Cited by 13 | Viewed by 2702

Abstract

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition. Full article

(This article belongs to the Special Issue New Trends in Intelligent Social-Health Systems Empowered by Internet of Every Things)

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Review

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23 pages, 3014 KiB

Open AccessReview

Past, Present and Future of Research on Wearable Technologies for Healthcare: A Bibliometric Analysis Using Scopus

by Yolanda-María de-la-Fuente-Robles, Adrián-Jesús Ricoy-Cano, Antonio-Pedro Albín-Rodríguez, José Luis López-Ruiz and Macarena Espinilla-Estévez

Sensors 2022, 22(22), 8599; https://0-doi-org.brum.beds.ac.uk/10.3390/s22228599 - 08 Nov 2022

Cited by 15 | Viewed by 7399

Abstract

Currently, wearable technology is present in different fields that aim to satisfy our needs in daily life, including the improvement of our health in general, the monitoring of patient health, ensuring the safety of people in the workplace or supporting athlete training. The objective of this bibliometric analysis is to examine and map the scientific advances in wearable technologies in healthcare, as well as to identify future challenges within this field and put forward some proposals to address them. In order to achieve this objective, a search of the most recent related literature was carried out in the Scopus database. Our results show that the research can be divided into two periods: before 2013, it focused on design and development of sensors and wearable systems from an engineering perspective and, since 2013, it has focused on the application of this technology to monitoring health and well-being in general, and in alignment with the Sustainable Development Goals wherever feasible. Our results reveal that the United States has been the country with the highest publication rates, with 208 articles (34.7%). The University of California, Los Angeles, is the institution with the most studies on this topic, 19 (3.1%). Sensors journal (Switzerland) is the platform with the most studies on the subject, 51 (8.5%), and has one of the highest citation rates, 1461. We put forward an analysis of keywords and, more specifically, a pennant chart to illustrate the trends in this field of research, prioritizing the area of data collection through wearable sensors, smart clothing and other forms of discrete collection of physiological data. Full article

(This article belongs to the Special Issue New Trends in Intelligent Social-Health Systems Empowered by Internet of Every Things)

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37 pages, 1983 KiB

Open AccessReview

Human Activity Recognition Data Analysis: History, Evolutions, and New Trends

by Paola Patricia Ariza-Colpas, Enrico Vicario, Ana Isabel Oviedo-Carrascal, Shariq Butt Aziz, Marlon Alberto Piñeres-Melo, Alejandra Quintero-Linero and Fulvio Patara

Sensors 2022, 22(9), 3401; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093401 - 29 Apr 2022

Cited by 22 | Viewed by 6820

Abstract

The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems—ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities. Full article

(This article belongs to the Special Issue New Trends in Intelligent Social-Health Systems Empowered by Internet of Every Things)

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37 pages, 5418 KiB

Open AccessReview

Application of IoT in Healthcare: Keys to Implementation of the Sustainable Development Goals

by Ángeles Verdejo Espinosa, José Lopez Ruiz, Francisco Mata Mata and Macarena Espinilla Estevez

Sensors 2021, 21(7), 2330; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072330 - 26 Mar 2021

Cited by 39 | Viewed by 10150

Abstract

We live in complex times in the health, social, political, and energy spheres, and we must be aware of and implement new trends in intelligent social health systems powered by the Internet of Things (IoT). Sustainable development, energy efficiency, and public health are interrelated parameters that can transform a system or an environment for the benefit of people and the planet. The integration of sensors and smart devices should promote energy efficiency and ensure that sustainable development goals are met. This work is carried out according to a mixed approach, with a literature review and an analysis of the impact of the Sustainable Development Goals on the applications of the Internet of Things and smart systems. In the analysis of results, the following questions are answered about these systems and applications: (a) Are IoT applications key to the improvement of people’s health and the environment? (b) Are there research and case studies implemented in cities or territories that demonstrate the effectiveness of IoT applications and their benefits to public health? (c) What sustainable development indicators and objectives can be assessed in the applications and projects analyzed? Full article

(This article belongs to the Special Issue New Trends in Intelligent Social-Health Systems Empowered by Internet of Every Things)

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