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Wireless Sensors Networks and Artificial Intelligence for Intelligent Health Monitoring
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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: closed (31 January 2022) | Viewed by 16567

Special Issue Editors

Prof. Dr. Antonio Coronato

E-Mail Website
Guest Editor
Research Center on ICT Technologies for Healthcare and Wellbeing, Università Telematica Giustino Fortunato, 82100 Benevento, Italy
Interests: artificial intelligence; self-learning systems; advanced systems for healthcare
Dr. Giovanni Paragliola

E-Mail Website
Co-Guest Editor
Institute of High Performance Computing and Networking, National Research Council of Italy, 80131 Naples, Italy
Interests: federated learning; deep learning; signal processing
Special Issues, Collections and Topics in MDPI journals

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 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. Journal of Sensor and Actuator Networks 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 2000 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 (4 papers)

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Research

14 pages, 323 KiB  
Article
Sensitivity of Machine Learning Approaches to Fake and Untrusted Data in Healthcare Domain
by Fiammetta Marulli, Stefano Marrone and Laura Verde
J. Sens. Actuator Netw. 2022, 11(2), 21; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan11020021 - 30 Mar 2022
Cited by 2 | Viewed by 2436
Abstract
Machine Learning models are susceptible to attacks, such as noise, privacy invasion, replay, false data injection, and evasion attacks, which affect their reliability and trustworthiness. Evasion attacks, performed to probe and identify potential ML-trained models’ vulnerabilities, and poisoning attacks, performed to obtain skewed [...] Read more.
Machine Learning models are susceptible to attacks, such as noise, privacy invasion, replay, false data injection, and evasion attacks, which affect their reliability and trustworthiness. Evasion attacks, performed to probe and identify potential ML-trained models’ vulnerabilities, and poisoning attacks, performed to obtain skewed models whose behavior could be driven when specific inputs are submitted, represent a severe and open issue to face in order to assure security and reliability to critical domains and systems that rely on ML-based or other AI solutions, such as healthcare and justice, for example. In this study, we aimed to perform a comprehensive analysis of the sensitivity of Artificial Intelligence approaches to corrupted data in order to evaluate their reliability and resilience. These systems need to be able to understand what is wrong, figure out how to overcome the resulting problems, and then leverage what they have learned to overcome those challenges and improve their robustness. The main research goal pursued was the evaluation of the sensitivity and responsiveness of Artificial Intelligence algorithms to poisoned signals by comparing several models solicited with both trusted and corrupted data. A case study from the healthcare domain was provided to support the pursued analyses. The results achieved with the experimental campaign were evaluated in terms of accuracy, specificity, sensitivity, F1-score, and ROC area. Full article
(This article belongs to the Special Issue Wireless Sensors Networks and Artificial Intelligence for Intelligent Health Monitoring)
14 pages, 1223 KiB  
Article
An AI-Empowered Home-Infrastructure to Minimize Medication Errors
by Muddasar Naeem and Antonio Coronato
J. Sens. Actuator Netw. 2022, 11(1), 13; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan11010013 - 09 Feb 2022
Cited by 6 | Viewed by 3815
Abstract
This article presents an Artificial Intelligence (AI)-based infrastructure to reduce medication errors while following a treatment plan at home. The system, in particular, assists patients who have some cognitive disability. The AI-based system first learns the skills of a patient using the Actor–Critic [...] Read more.
This article presents an Artificial Intelligence (AI)-based infrastructure to reduce medication errors while following a treatment plan at home. The system, in particular, assists patients who have some cognitive disability. The AI-based system first learns the skills of a patient using the Actor–Critic method. After assessing patients’ disabilities, the system adopts an appropriate method for the monitoring process. Available methods for monitoring the medication process are a Deep Learning (DL)-based classifier, Optical Character Recognition, and the barcode technique. The DL model is a Convolutional Neural Network (CNN) classifier that is able to detect a drug even when shown in different orientations. The second technique is an OCR based on Tesseract library that reads the name of the drug from the box. The third method is a barcode based on Zbar library that identifies the drug from the barcode available on the box. The GUI demonstrates that the system can assist patients in taking the correct drug and prevent medication errors. This integration of three different tools to monitor the medication process shows advantages as it decreases the chance of medication errors and increases the chance of correct detection. This methodology is more useful when a patient has mild cognitive impairment. Full article
(This article belongs to the Special Issue Wireless Sensors Networks and Artificial Intelligence for Intelligent Health Monitoring)
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27 pages, 3761 KiB  
Article
A Study of Fall Detection in Assisted Living: Identifying and Improving the Optimal Machine Learning Method
by Nirmalya Thakur and Chia Y. Han
J. Sens. Actuator Netw. 2021, 10(3), 39; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10030039 - 24 Jun 2021
Cited by 63 | Viewed by 6543
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
(This article belongs to the Special Issue Wireless Sensors Networks and Artificial Intelligence for Intelligent Health Monitoring)
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17 pages, 1491 KiB  
Article
A Highly Effective Route for Real-Time Traffic Using an IoT Smart Algorithm for Tele-Surgery Using 5G Networks
by Radwan S. Abujassar, Husam Yaseen and Ahmad Samed Al-Adwan
J. Sens. Actuator Netw. 2021, 10(2), 30; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan10020030 - 22 Apr 2021
Cited by 13 | Viewed by 2724
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
(This article belongs to the Special Issue Wireless Sensors Networks and Artificial Intelligence for Intelligent Health Monitoring)
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