Special Issue "Wireless Sensors Networks and Artificial Intelligence for Intelligent Health Monitoring"

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Big Data, Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Dr. Antonio Coronato
E-Mail Website
Guest Editor
Institute for High Performance Computing and Networking—National Research Council, Naples, Italy
Interests: health monitoring systems; artificial intelligence; self-learning systems
Dr. Giovanni Paragliola
E-Mail Website
Co-Guest Editor
Institute for High Performance Computing and Networking—National Research Council, Naples, Italy
Interests: human behaviour analysis; deep learning; IoT

Special Issue Information

Dear Colleagues,

Wireless sensor networks, body sensor networks, Internet of Things, and artificial intelligence technologies are paving the way towards a new class of intelligent health monitoring applications. Each one of these fields has definitively improved over the last years; however, it is just their integration that is making health monitoring applications ever more intelligent, effective, efficient, and reliable.

This Special Issue is intended to report on new classes of applications in the healthcare domain that benefit from the integration of such technologies. In this context, we are envisaging works covering one or more of the following topics:

  • Vital-signs intelligent monitoring;
  • Patient critical-condition identification and prevention;
  • Patient behavior analysis;
  • Mood recognition;
  • Ambient-assisted living;
  • WSNs and AI for healthy aging;
  • Methodologies and tools for the rapid integration of WSN, IoT, and AI in health monitoring;
  • Monitoring systems within the medical devices contest.

Dr. Antonio Coronato
Dr. Giovanni Paragliola
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 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. Journal of Sensor and Actuator Networks is an international peer-reviewed open access quarterly 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.

Keywords

  • machine learning
  • deep learning
  • reinforcement learning
  • body sensor networks
  • medical devices
  • IoT
  • behavior analysis
  • eHealth
  • precision medicine

Published Papers (2 papers)

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Research

Article
A Study of Fall Detection in Assisted Living: Identifying and Improving the Optimal Machine Learning Method
J. Sens. Actuator Netw. 2021, 10(3), 39; https://doi.org/10.3390/jsan10030039 - 24 Jun 2021
Cited by 3 | Viewed by 810
Abstract
This paper makes four scientific contributions to the field of fall detection in the elderly to contribute to their assisted living in the future of Internet of Things (IoT)-based pervasive living environments, such as smart homes. First, it presents and discusses a comprehensive [...] Read more.
This paper makes four scientific contributions to the field of fall detection in the elderly to contribute to their assisted living in the future of Internet of Things (IoT)-based pervasive living environments, such as smart homes. First, it presents and discusses a comprehensive comparative study, where 19 different machine learning methods were used to develop fall detection systems, to deduce the optimal machine learning method for the development of such systems. This study was conducted on two different datasets, and the results show that out of all the machine learning methods, the k-NN classifier is best suited for the development of fall detection systems in terms of performance accuracy. Second, it presents a framework that overcomes the limitations of binary classifier-based fall detection systems by being able to detect falls and fall-like motions. Third, to increase the trust and reliance on fall detection systems, it introduces a novel methodology based on the usage of k-folds cross-validation and the AdaBoost algorithm that improves the performance accuracy of the k-NN classifier-based fall detection system to the extent that it outperforms all similar works in this field. This approach achieved performance accuracies of 99.87% and 99.66%, respectively, when evaluated on the two datasets. Finally, the proposed approach is also highly accurate in detecting the activity of standing up from a lying position to infer whether a fall was followed by a long lie, which can cause minor to major health-related concerns. The above contributions address multiple research challenges in the field of fall detection, that we identified after conducting a comprehensive review of related works, which is also presented in this paper. Full article
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Article
A Highly Effective Route for Real-Time Traffic Using an IoT Smart Algorithm for Tele-Surgery Using 5G Networks
J. Sens. Actuator Netw. 2021, 10(2), 30; https://doi.org/10.3390/jsan10020030 - 22 Apr 2021
Viewed by 629
Abstract
Nowadays, networks use many different paths to exchange data. However, our research will construct a reliable path in the networks among a huge number of nodes for use in tele-surgery using medical applications such as healthcare tracking applications, including tele-surgery which lead to [...] Read more.
Nowadays, networks use many different paths to exchange data. However, our research will construct a reliable path in the networks among a huge number of nodes for use in tele-surgery using medical applications such as healthcare tracking applications, including tele-surgery which lead to optimizing medical quality of service (m-QoS) during the COVID-19 situation. Many people could not travel due to the current issues, for fear of spreading the covid-19 virus. Therefore, our paper will provide a very trusted and reliable method of communication between a doctor and his patient so that the latter can do his operation even from a far distance. The communication between the doctor and his/her patient will be monitored by our proposed algorithm to make sure that the data will be received without delay. We test how we can invest buffer space that can be used efficiently to reduce delays between source and destination, avoiding loss of high-priority data packets. The results are presented in three stages. First, we show how to obtain the greatest possible reduction in rate variability when the surgeon begins an operation using live streaming. Second, the proposed algorithm reduces congestion on the determined path used for the online surgery. Third, we have evaluated the affection of optimal smoothing algorithm on the network parameters such as peak-to-mean ratio and delay to optimize m-QoS. We propose a new Smart-Rout Control algorithm (s-RCA) for creating a virtual smart path between source and destination to transfer the required data traffic between them, considering the number of hops and link delay. This provides a reliable connection that can be used in healthcare surgery to guarantee that all instructions are received without any delay, to be executed instantly. This idea can improve m-QoS in distance surgery, with trusted paths. The new s-RCA can be adapted with an existing routing protocol to track the primary path and monitor emergency packets received in node buffers, for direct forwarding via the demand path, with extended features. Full article
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