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Internet of Medical Things and Smart Healthcare
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Internet of Medical Things and Smart Healthcare

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

Deadline for manuscript submissions: 30 August 2024 | Viewed by 9346

Special Issue Editors

Dr. Francesco Lamonaca

E-Mail Website
Guest Editor
1. Department of Computer Engineering, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, 87036 Rende, CS, Italy
2. CNR-NANOTEC, 87036 Rende, CS, Italy
Interests: measurements; distributed measurement systems; measurement and monitoring systems based on the IoT; measurement and monitoring systems based on AI; wireless sensor network; synchronization of measurement instruments and sensors; non-invasive measurements; non-destructive testing
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Picariello

E-Mail Website
Guest Editor
Department of Engineering, University of Sannio, 82100 Benevento, Italy
Interests: electrical and electronic instrumentation; data acquisition systems (DAQs) based on compressive sampling (CS); biomedical instrumentation; distributed measurement systems, including wireless sensor networks (WSNs); Internet of Things (IoT)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) was proposed as new paradigm for machine-to-machine communication, whereby the things (i.e. smart sensors/actuators) are augmented with internet connectivity.

Biomedical systems adopting the IoT (i.e., forming internet-connected instrumentation for patient monitoring) are currently used in wearable health monitoring systems, providing personalized healthcare and telemedicine services.

This Special Issue aims to collect recent advances in the development of IoT systems applied to the medical field. High-quality research articles, as well as reviews, are welcome.

Of special interest is research work addressing recent developments in new technology to implement wearable medical devices, wireless sensor networks for medical purposes, compressed sensing applied to biomedical signals, wireless and battery-powered devices for biomedical applications, and artificial intelligence applied to biomedical signals.

Topics of interest include, but are not limited to, the following themes:

  • Internet of Medical Things;
  • Wearable devices;
  • Smart healthcare systems;
  • Compressed sensing for biomedical signals;
  • Biomedical measurement systems;
  • Artificial intelligence for biomedical applications;
  • Calibration of biomedical devices.

Dr. Francesco Lamonaca
Dr. Francesco Picariello
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.

Published Papers (4 papers)

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Research

16 pages, 7261 KiB  
Article
Design and Validity of a Smart Healthcare and Control System for Electric Bikes
by Eli Gabriel Avina-Bravo, Felipe Augusto Sodre Ferreira de Sousa, Christophe Escriba, Pascal Acco, Franck Giraud, Jean-Yves Fourniols and Georges Soto-Romero
Sensors 2023, 23(8), 4079; https://doi.org/10.3390/s23084079 - 18 Apr 2023
Cited by 3 | Viewed by 1777
Abstract
This paper presents the development of an electronic system that converts an electrically assisted bicycle into an intelligent health monitoring system, allowing people who are not athletic or who have a history of health issues to progressively start the physical activity by following [...] Read more.
This paper presents the development of an electronic system that converts an electrically assisted bicycle into an intelligent health monitoring system, allowing people who are not athletic or who have a history of health issues to progressively start the physical activity by following a medical protocol (e.g., max heart rate and power output, training time). The developed system aims to monitor the health state of the rider, analyze data in real-time, and provide electric assistance, thus diminishing muscular exertion. Furthermore, such a system can recover the same physiological data used in medical centers and program it into the e-bike to track the patient’s health. System validation is conducted by replicating a standard medical protocol used in physiotherapy centers and hospitals, typically conducted in indoor conditions. However, the presented work differentiates itself by implementing this protocol in outdoor environments, which is impossible with the equipment used in medical centers. The experimental results show that the developed electronic prototypes and the algorithm effectively monitored the subject’s physiological condition. Moreover, when necessary, the system can change the training load and help the subject remain in their prescribed cardiac zone. This system allows whoever needs to follow a rehabilitation program to do so not only in their physician’s office, but whenever they want, including while commuting. Full article
(This article belongs to the Special Issue Internet of Medical Things and Smart Healthcare)
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16 pages, 5425 KiB  
Article
Research on Medical Security System Based on Zero Trust
by Zhiqiang Wang, Xinyue Yu, Peiyang Xue, Yunhan Qu and Lei Ju
Sensors 2023, 23(7), 3774; https://doi.org/10.3390/s23073774 - 06 Apr 2023
Cited by 2 | Viewed by 1667
Abstract
With the rapid development of Internet of Things technology, cloud computing, and big data, the combination of medical systems and information technology has become increasingly close. However, the emergence of intelligent medical systems has brought a series of network security threats and hidden [...] Read more.
With the rapid development of Internet of Things technology, cloud computing, and big data, the combination of medical systems and information technology has become increasingly close. However, the emergence of intelligent medical systems has brought a series of network security threats and hidden dangers, including data leakage and remote attacks, which can directly threaten patients’ lives. To ensure the security of medical information systems and expand the application of zero trust in the medical field, we combined the medical system with the zero-trust security system to propose a zero-trust medical security system. In addition, in its dynamic access control module, based on the RBAC model and the calculation of user behavior risk value and trust, an access control model based on subject behavior evaluation under zero-trust conditions (ABEAC) was designed to improve the security of medical equipment and data. Finally, the feasibility of the system is verified through a simulation experiment. Full article
(This article belongs to the Special Issue Internet of Medical Things and Smart Healthcare)
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28 pages, 16269 KiB  
Article
FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices
by Mahbub Ul Alam and Rahim Rahmani
Sensors 2023, 23(2), 970; https://doi.org/10.3390/s23020970 - 14 Jan 2023
Cited by 7 | Viewed by 3229
Abstract
The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus [...] Read more.
The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision–recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity of electronic health records. Multimodality with the generative adversarial neural networks provides the best result: the area under the precision–recall curve is 96.55%, the area under the receiver operating characteristic curve is 99.35%, and earliness is 4.56 h. FedSepsis suggests that incorporating such a concept together with low-end computational devices could be beneficial for all the medical sector stakeholders and should be explored further. Full article
(This article belongs to the Special Issue Internet of Medical Things and Smart Healthcare)
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25 pages, 5118 KiB  
Article
Prioritization-Driven Congestion Control in Networks for the Internet of Medical Things: A Cross-Layer Proposal
by Raymundo Buenrostro-Mariscal, Pedro C. Santana-Mancilla, Osval Antonio Montesinos-López, Mabel Vazquez-Briseno and Juan Ivan Nieto-Hipolito
Sensors 2023, 23(2), 923; https://doi.org/10.3390/s23020923 - 13 Jan 2023
Cited by 6 | Viewed by 1856
Abstract
Real-life implementation of the Internet of Things (IoT) in healthcare requires sufficient quality of service (QoS) to transmit the collected data successfully. However, unsolved challenges in prioritization and congestion issues limit the functionality of IoT networks by increasing the likelihood of packet loss, [...] Read more.
Real-life implementation of the Internet of Things (IoT) in healthcare requires sufficient quality of service (QoS) to transmit the collected data successfully. However, unsolved challenges in prioritization and congestion issues limit the functionality of IoT networks by increasing the likelihood of packet loss, latency, and high-power consumption in healthcare systems. This study proposes a priority-based cross-layer congestion control protocol called QCCP, which is managed by communication devices’ transport and medium access control (MAC) layers. Unlike existing methods, the novelty of QCCP is how it estimates and resolves wireless channel congestion because it does not generate control packets, operates in a distributed manner, and only has a one-bit overhead. Furthermore, at the same time, QCCP offers packet scheduling considering each packet’s network load and QoS. The results of the experiments demonstrated that with a 95% confidence level, QCCP achieves sufficient performance to support the QoS requirements for the transmission of health signals. Finally, the comparison study shows that QCCP outperforms other TCP protocols, with 64.31% higher throughput, 18.66% less packet loss, and 47.87% less latency. Full article
(This article belongs to the Special Issue Internet of Medical Things and Smart Healthcare)
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