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Sensor and Systems Evaluation for Telemedicine and eHealth
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Sensor and Systems Evaluation for Telemedicine and eHealth

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

Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 31407

Special Issue Editors

Dr. Isabel De la Torre Díez

E-Mail Website
Guest Editor
Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
Interests: telemedicine; eHealth; mHealth; healthcare systems evaluation; QoE; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Sofiane Hamrioui

E-Mail Website
Guest Editor
Affiliation: Institut d'Électronique et de Télécommunications de Rennes, Rennes, France
Interests: QoS; QoE; eHealth; mobile computing; Wireless Sensor Networks; IoT

Special Issue Information

Dear Colleagues,

This Special Issue aims to collect advances in sensors in the healthcare environment and in the evaluation of all types of telemedicine and eHealth systems and applications. Both original research and review papers are welcome.

At present, the prevalence of broadband wireless access networks has made it possible for users to remain connected to the Internet in almost all places. This almost-ubiquitous connectivity available has created new possibilities for innovative eHealth applications and services. The ability of a network to meet the implicit and indicated connection needs of users is specified in terms of quality of service (QoS) and the general acceptability of an application or service, as subjectively perceived by the end user as quality of experience (QoE). It is important to monitor QoS in a telemedicine network of storage and cases retransmission, to establish QoS parameters in terms of wireless telemedicine for eHealth services, and to evaluate the quality of medical images and the performance metrics in the assignment of resources oriented to QoE for health monitoring. With the Internet of Things (IoT), the evaluation of corporal detection devices in networks is key. These devices store individuals’ health information in real time in addition to their related behavior, where privacy and security must be considered.

The MDPI journal Sensors is soliciting paper submissions, and aims to bring together researchers and application developers working on the intersection of IoT sensors in eHealth such as sensor design and development, distributed, cloud, internet, mobile, ambient, real-time, secure, and privacy-preserving computing related to eHealth. We also aim to explore the application of novel IoT computing results in eHealth.

Dr. Isabel De la Torre Díez
Dr. Sofiane Hamrioui
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

  • Wireless Sensor Networks
  • Telemedicine
  • eHealth
  • Healthcare systems evaluation: QoS and QoE
  • Privacy and security in WSNs

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Published Papers (6 papers)

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Research

17 pages, 3763 KiB  
Article
Measurement and Modeling of Microbial Growth Using Timelapse Video
by Konstantinos Delibasis, Ifigenia Basanou and Alexandros-Apostolos A. Boulogeorgos
Sensors 2020, 20(9), 2545; https://doi.org/10.3390/s20092545 - 29 Apr 2020
Cited by 1 | Viewed by 3210
Abstract
The development of timelapse videos for the investigation of growing microbial colonies has gained increasing interest due to its low cost and complexity implementation. In the present study, a simple experimental setup is proposed for periodic snapshot acquisition of a petri dish cultivating [...] Read more.
The development of timelapse videos for the investigation of growing microbial colonies has gained increasing interest due to its low cost and complexity implementation. In the present study, a simple experimental setup is proposed for periodic snapshot acquisition of a petri dish cultivating a fungus of the genus Candida SPP, thus creating a timelapse video. A computational algorithm, based on image processing techniques is proposed for estimating the microbial population and for extracting the experimental population curves, showing the time evolution of the population of microbes at any region of the dish. Likewise, a novel mathematical population evolution modeling approach is reported, which is based on the logistic function (LF). Parameter estimation of the aforementioned model is described and visually assessed, in comparison with the conventional and widely-used LF method. The effect of the image analysis parameterization is also highlighted. Our experiments take into account different area sizes, i.e., the number of pixels in the neighborhood, to generate population curves and calculate the model parameters. Our results reveal that, as the size of the area increases, the curve becomes smoother, the signal-to-noise-ratio increases and the estimation of model parameters becomes more accurate. Full article
(This article belongs to the Special Issue Sensor and Systems Evaluation for Telemedicine and eHealth)
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17 pages, 1914 KiB  
Article
Portable Ultrasound-Based Device for Detecting Older Adults’ Sit-to-Stand Transitions in Unsupervised 30-Second Chair–Stand Tests
by Antonio Cobo, Elena Villalba-Mora, Dieter Hayn, Xavier Ferre, Rodrigo Pérez-Rodríguez, Alberto Sánchez-Sánchez, Raquel Bernabé-Espiga, Juan-Luis Sánchez-Sánchez, Andrea López-Diez-Picazo, Cristian Moral and Leocadio Rodriguez-Mañas
Sensors 2020, 20(7), 1975; https://doi.org/10.3390/s20071975 - 1 Apr 2020
Cited by 9 | Viewed by 3512
Abstract
Lower-limb strength is a marker of functional decline in elders. This work studies the feasibility of using the quasi-periodic nature of the distance between a subjects’ back and the chair backrest during a 30-s chair–stand test (CST) to carry out unsupervised measurements based [...] Read more.
Lower-limb strength is a marker of functional decline in elders. This work studies the feasibility of using the quasi-periodic nature of the distance between a subjects’ back and the chair backrest during a 30-s chair–stand test (CST) to carry out unsupervised measurements based on readings from a low-cost ultrasound sensor. The device comprises an ultrasound sensor, an Arduino UNO board, and a Bluetooth module. Sit-to-stand transitions are identified by filtering the signal with a moving minimum filter and comparing the output to an adaptive threshold. An inter-rater reliability (IRR) study was carried out to validate the device ability to count the same number of valid transitions as the gold-standard manual count. A group of elders (age: mean (m) = 80.79 years old, SD = 5.38; gender: 21 female and seven male) were asked to perform a 30-s CST using the device while a trained nurse manually counted valid transitions. Ultimately, a moving minimum filter was necessary to cancel the effect of outliers, likely produced because older people tend to produce more motion artefacts and, thus, noisier signals. While the intra-class correlation coefficient (ICC) for this study was good (ICC = 0.86, 95% confidence interval (CI) = 0.73, 0.93), it is not yet clear whether the results are sufficient to support clinical decision-making. Full article
(This article belongs to the Special Issue Sensor and Systems Evaluation for Telemedicine and eHealth)
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20 pages, 893 KiB  
Article
A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications
by Hassan Harb, Hussein Mroue, Ali Mansour, Abbass Nasser and Eduardo Motta Cruz
Sensors 2020, 20(7), 1931; https://doi.org/10.3390/s20071931 - 30 Mar 2020
Cited by 29 | Viewed by 6717
Abstract
Nowadays, the increasing number of patients accompanied with the emergence of new symptoms and diseases makes heath monitoring and assessment a complicated task for medical staff and hospitals. Indeed, the processing of big and heterogeneous data collected by biomedical sensors along with the [...] Read more.
Nowadays, the increasing number of patients accompanied with the emergence of new symptoms and diseases makes heath monitoring and assessment a complicated task for medical staff and hospitals. Indeed, the processing of big and heterogeneous data collected by biomedical sensors along with the need of patients’ classification and disease diagnosis become major challenges for several health-based sensing applications. Thus, the combination between remote sensing devices and the big data technologies have been proven as an efficient and low cost solution for healthcare applications. In this paper, we propose a robust big data analytics platform for real time patient monitoring and decision making to help both hospital and medical staff. The proposed platform relies on big data technologies and data analysis techniques and consists of four layers: real time patient monitoring, real time decision and data storage, patient classification and disease diagnosis, and data retrieval and visualization. To evaluate the performance of our platform, we implemented our platform based on the Hadoop ecosystem and we applied the proposed algorithms over real health data. The obtained results show the effectiveness of our platform in terms of efficiently performing patient classification and disease diagnosis in healthcare applications. Full article
(This article belongs to the Special Issue Sensor and Systems Evaluation for Telemedicine and eHealth)
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19 pages, 4007 KiB  
Article
MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management
by Ammar Awad Mutlag, Mohd Khanapi Abd Ghani, Mazin Abed Mohammed, Mashael S. Maashi, Othman Mohd, Salama A. Mostafa, Karrar Hameed Abdulkareem, Gonçalo Marques and Isabel de la Torre Díez
Sensors 2020, 20(7), 1853; https://doi.org/10.3390/s20071853 - 27 Mar 2020
Cited by 89 | Viewed by 5344
Abstract
In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That [...] Read more.
In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks’ priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data. Full article
(This article belongs to the Special Issue Sensor and Systems Evaluation for Telemedicine and eHealth)
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11 pages, 344 KiB  
Article
Feasibility of Social-Network-Based eHealth Intervention on the Improvement of Healthy Habits among Children
by José Alberto Benítez-Andrades, Natalia Arias, María Teresa García-Ordás, Marta Martínez-Martínez and Isaías García-Rodríguez
Sensors 2020, 20(5), 1404; https://doi.org/10.3390/s20051404 - 4 Mar 2020
Cited by 15 | Viewed by 4399
Abstract
This study shows the feasibility of an eHealth solution for tackling eating habits and physical activity in the adolescent population. The participants were children from 11 to 15 years old. An intervention was carried out on 139 students in the intervention group and [...] Read more.
This study shows the feasibility of an eHealth solution for tackling eating habits and physical activity in the adolescent population. The participants were children from 11 to 15 years old. An intervention was carried out on 139 students in the intervention group and 91 students in the control group, in two schools during 14 weeks. The intervention group had access to the web through a user account and a password. They were able to create friendship relationships, post comments, give likes and interact with other users, as well as receive notifications and information about nutrition and physical activity on a daily basis and get (virtual) rewards for improving their habits. The control group did not have access to any of these features. The homogeneity of the samples in terms of gender, age, body mass index and initial health-related habits was demonstrated. Pre- and post-measurements were collected through self-reports on the application website. After applying multivariate analysis of variance, a significant alteration in the age-adjusted body mass index percentile was observed in the intervention group versus the control group, as well as in the PAQ-A score and the KIDMED score. It can be concluded that eHealth interventions can help to obtain healthy habits. More research is needed to examine the effectiveness in achieving adherence to these new habits. Full article
(This article belongs to the Special Issue Sensor and Systems Evaluation for Telemedicine and eHealth)
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16 pages, 2130 KiB  
Article
Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data
by María Teresa García-Ordás, José Alberto Benítez-Andrades, Isaías García-Rodríguez, Carmen Benavides and Héctor Alaiz-Moretón
Sensors 2020, 20(4), 1214; https://doi.org/10.3390/s20041214 - 22 Feb 2020
Cited by 80 | Viewed by 7362
Abstract
The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases [...] Read more.
The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification. Full article
(This article belongs to the Special Issue Sensor and Systems Evaluation for Telemedicine and eHealth)
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