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Special Issue "Smart Sensor Networks and Technology for Healthcare Monitoring and Decision Making"

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

Deadline for manuscript submissions: closed (30 December 2017).

Special Issue Editor

Prof. Miltiadis D. Lytras
E-Mail Website
Guest Editor
1. School of Business, Deree—The American College of Greece, 6 Gravias Street GR-153 42 Aghia Paraskevi, Athens, Greece
2. Effat University, Jeddah, Saudi Arabia
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management; semantic web
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The current model of healthcare in the world is subject to unprecedented challenges. An ageing population, inadequacies of medical personnel, the impact of lifestyle factors and increasing expenditure mean that the existing approaches may become unsustainable. This gives an impetus to promote sensor networks and technology for healthcare monitoring. More importantly, the sensor data obtained from healthcare monitoring must be further analyzed and interpreted to ultimately make decisions.

The objective of this Special Issue is to disseminate the latest, high quality, interdisciplinary research in the domain of sensor networks and technology for healthcare monitoring and to propose methodology for decision making. This will provide a smart solution to relieve the workload of medical personnel and increase the quality of life of human beings.

For this Special Issue, we welcome articles, reviews and short communications relating to the state-of-the-art, system architecture, and artificial intelligence. A pilot study or large-scale deployment is extremely welcome. Examples of topics of interest include, but are not limited to:

Studies on sensor networks and technology for healthcare monitoring

  • Coexistence of wired and wireless sensor networks for the healthcare system
  • Wearable sensing for healthcare
  • Case studies (including a pilot study) in medical industries
  • Healthcare monitoring and its decision-making process
  • Internet-of-Things, big data analysis in sensor networks and technology for healthcare
  • Sensor network optimization in the healthcare system
  • Critical applications in healthcare sensor networks
  • Sensors for ambient-assisted-living

Prof. Miltiadis D. Lytras
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 papers will be 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 1800 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

  • Ambient-assisted-living
  • Artificial intelligence
  • Critical applications
  • Decision making
  • Healthcare monitoring
  • Interoperability
  • Optimization
  • Patient monitoring
  • Pilot study
  • Quality of life
  • Sensor network
  • Sensor technology
  • Smart Innovation
  • System reliability
  • Wireless technology

Published Papers (4 papers)

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Research

Open AccessArticle
Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes
Sensors 2018, 18(1), 79; https://doi.org/10.3390/s18010079 - 29 Dec 2017
Cited by 3
Abstract
Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the [...] Read more.
Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects. Full article
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Open AccessArticle
Self-Organizing Peer-To-Peer Middleware for Healthcare Monitoring in Real-Time
Sensors 2017, 17(11), 2650; https://doi.org/10.3390/s17112650 - 17 Nov 2017
Cited by 2
Abstract
As the number of elderly persons with chronic illnesses increases, a new public infrastructure for their care is becoming increasingly necessary. In particular, technologies that can monitoring bio-signals in real-time have been receiving significant attention. Currently, most healthcare monitoring services are implemented by [...] Read more.
As the number of elderly persons with chronic illnesses increases, a new public infrastructure for their care is becoming increasingly necessary. In particular, technologies that can monitoring bio-signals in real-time have been receiving significant attention. Currently, most healthcare monitoring services are implemented by wireless carrier through centralized servers. These services are vulnerable to data concentration because all data are sent to a remote server. To solve these problems, we propose self-organizing P2P middleware for healthcare monitoring that enables a real-time multi bio-signal streaming without any central server by connecting the caregiver and care recipient. To verify the performance of the proposed middleware, we evaluated the monitoring service matching time based on a monitoring request. We also confirmed that it is possible to provide an effective monitoring service by evaluating the connectivity between Peer-to-Peer and average jitter. Full article
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Open AccessArticle
An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
Sensors 2017, 17(10), 2302; https://doi.org/10.3390/s17102302 - 10 Oct 2017
Cited by 37
Abstract
The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on [...] Read more.
The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries. Full article
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Open AccessArticle
Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination
Sensors 2017, 17(10), 2167; https://doi.org/10.3390/s17102167 - 21 Sep 2017
Cited by 4
Abstract
Microscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. Rapid, accurate, accessible diagnostic tools are increasingly [...] Read more.
Microscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. Rapid, accurate, accessible diagnostic tools are increasingly required, as malaria control programs extend parasite-based diagnosis and the prevalence decreases. This paper presents an image processing and analysis methodology using supervised classification to assess the presence of malaria parasites and determine the species and life cycle stage in Giemsa-stained thin blood smears. The main differentiation factor is the usage of microscopic images exclusively acquired with low cost and accessible tools such as smartphones, a dataset of 566 images manually annotated by an experienced parasilogist being used. Eight different species-stage combinations were considered in this work, with an automatic detection performance ranging from 73.9% to 96.2% in terms of sensitivity and from 92.6% to 99.3% in terms of specificity. These promising results attest to the potential of using this approach as a valid alternative to conventional microscopy examination, with comparable detection performances and acceptable computational times. Full article
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