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Electronics
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Internet of Things, Embedded Solutions, and Edge Intelligence for Smart...
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Internet of Things, Embedded Solutions, and Edge Intelligence for Smart Health

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 4493

Special Issue Editors

Dr. Ilaria Sergi

E-Mail Website
Guest Editor
Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy
Interests: Internet of Things; smart environments; edge intelligence; middleware and architecture for the IoT; mobile applications and rapid prototyping in the IoT; smart devices and intelligent products
Dr. Teodoro Montanaro

E-Mail Website
Guest Editor
Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy
Interests: Internet of Things; smart environments; edge intelligence; middleware and architecture for the IoT; mobile applications and rapid prototyping in the IoT; smart devices and intelligent products

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) solutions are becoming increasingly popular in the healthcare domain due to their ability to monitor patients' health status remotely in real time. Moreover, the integration of IoT with edge intelligence solutions allows for devices to process and analyze all data locally instead of sending it all to a central location. This is particularly important in applications where real-time decision making or low latency is required. In the field of smart health, IoT solutions can play a crucial role in the monitoring and management of the health of individuals. This can also include wearable/embedded devices that track vital signs, medical devices that monitor chronic conditions, and even smart home devices that can assist with medication management. By collecting and analyzing data from multiple sources, these solutions can provide a more complete picture of an individual's health and allow for more personalized and proactive healthcare.

This Special Issue aims to present interesting papers focused on the exploitation of the IoT and edge intelligence in the healthcare domain. Topics of interest include, but are not limited to:

  • Innovative healthcare architectures to improve patients' conditions;
  • Proposals to save costs, consumption, and improve the efficiency of healthcare systems;
  • Innovations for personalized healthcare;
  • Improved data collection and analysis;
  • Solutions that exploit real-time decision making in the healthcare domain;
  • Home-based healthcare assistance to reduce healthcare professionals' workload;
  • Edge intelligent solutions for the early detection of potential health issues;
  • IoT systems to promote the collection of more detailed and accurate information about patients' health;
  • Solutions to improve the communication and coordination among healthcare professionals in critical scenarios;
  • Innovative healthcare systems for providing prompt reactions and interventions in cases of emergency;
  • Innovative healthcare systems for specific chronic diseases;
  • The exploitation of energy-efficient designs and developments of devices for healthcare.

Dr. Ilaria Sergi
Dr. Teodoro Montanaro
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. Electronics 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 2400 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

  • Internet of Things
  • smart health
  • embedded solutions for health
  • edge intelligence in health systems
  • internet of medical things
  • homecare assistance
  • personalized healthcare

Published Papers (6 papers)

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Research

20 pages, 6513 KiB  
Article
An Explainable Method for Lung Cancer Detection and Localisation from Tissue Images through Convolutional Neural Networks
by Francesco Mercaldo, Myriam Giusy Tibaldi, Lucia Lombardi, Luca Brunese, Antonella Santone and Mario Cesarelli
Electronics 2024, 13(7), 1393; https://doi.org/10.3390/electronics13071393 - 07 Apr 2024
Viewed by 579
Abstract
Lung cancer, a prevalent and life-threatening condition, necessitates early detection for effective intervention. Considering the recent advancements in deep learning techniques, particularly in medical image analysis, which offer unparalleled accuracy and efficiency, in this paper, we propose a method for the automated identification [...] Read more.
Lung cancer, a prevalent and life-threatening condition, necessitates early detection for effective intervention. Considering the recent advancements in deep learning techniques, particularly in medical image analysis, which offer unparalleled accuracy and efficiency, in this paper, we propose a method for the automated identification of cancerous cells in lung tissue images. We explore various deep learning architectures with the objective of identifying the most effective one based on both quantitative and qualitative assessments. In particular, we assess qualitative outcomes by incorporating the concept of prediction explainability, enabling the visualization of areas within tissue images deemed relevant to the presence of lung cancer by the model. The experimental analysis, conducted on a dataset comprising 15,000 lung tissue images, demonstrates the effectiveness of our proposed method, yielding an accuracy rate of 0.99. Full article
(This article belongs to the Special Issue Internet of Things, Embedded Solutions, and Edge Intelligence for Smart Health)
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12 pages, 2662 KiB  
Article
A Wireless Potentiostat Exploiting PWM-DAC for Interfacing of Wearable Electrochemical Biosensors in Non-Invasive Monitoring of Glucose Level
by Antonio Vincenzo Radogna, Luca Francioso, Elisa Sciurti, Daniele Bellisario, Vanessa Esposito and Giuseppe Grassi
Electronics 2024, 13(6), 1128; https://doi.org/10.3390/electronics13061128 - 20 Mar 2024
Viewed by 552
Abstract
In this paper, a wireless potentiostat code-named ElectroSense, for interfacing of wearable electrochemical biosensors, will be presented. The system is devoted to non-invasive monitoring of glucose in wearable medical applications. Differently from other potentiostats in literature, which use digital-to-analog converters (DACs) as discrete [...] Read more.
In this paper, a wireless potentiostat code-named ElectroSense, for interfacing of wearable electrochemical biosensors, will be presented. The system is devoted to non-invasive monitoring of glucose in wearable medical applications. Differently from other potentiostats in literature, which use digital-to-analog converters (DACs) as discrete components or integrated in high-end microcontrollers, in this work the pulse width modulation (PWM) technique is exploited through PWM-DAC approach to generate signals. The ubiquitous presence of integrated PWM peripherals in low-end microcontrollers, which generally also integrate analog-to-digital converters (ADCs), enables both the generation and acquisition of read-out signals on a single cheap electronic device without additional hardware. By this way, system’s production costs, power consumption, and system’s size are greatly reduced with respect to other solutions. All these features allow the system’s adoption in wearable healthcare Internet-of-things (IoT) ecosystems. A description of both the sensing technology and the circuit will be discussed in detail, emphasizing advantages and drawbacks of the PWM-DAC approach. Experimental measurements will prove the efficacy of the proposed electronic system for non-invasive monitoring of glucose in wearable medical applications. Full article
(This article belongs to the Special Issue Internet of Things, Embedded Solutions, and Edge Intelligence for Smart Health)
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12 pages, 1173 KiB  
Article
Evaluation of Fatigue in Older Drivers Using a Multimodal Medical Sensor and Driving Simulator
by Yutaka Yoshida, Kohei Kowata, Ryotaro Abe and Emi Yuda
Electronics 2024, 13(6), 1126; https://doi.org/10.3390/electronics13061126 - 20 Mar 2024
Viewed by 496
Abstract
In recent years, the spread of wearable medical sensors has made it possible to easily measure biological signals such as pulse rate and body acceleration (BA), and from these biological signals, it is possible to evaluate the degree of biological stress and autonomic [...] Read more.
In recent years, the spread of wearable medical sensors has made it possible to easily measure biological signals such as pulse rate and body acceleration (BA), and from these biological signals, it is possible to evaluate the degree of biological stress and autonomic nervous activity in daily life. Accumulated fatigue due to all-day work and lack of sleep is thought to be a contributing factor to distracted driving, and technology to estimate fatigue from biological signals during driving is desired. In this study, we investigated fatigue evaluation during a driving simulator (DS) using biological information on seven older subjects. A DS experiment was performed in the morning and afternoon, and no significant differences were observed in the change over time of heart rate variability and skin temperature. On the other hand, in the afternoon DS, changes in arousal and body movements were observed based on BA calculated from the three-axis acceleration sensor and fingertip reaction time in a psychomotor vigilance test. It is suggested that by combining biological information, it may be possible to evaluate the degree of fatigue from the presence or absence of arousal and changes in body movements while driving. Full article
(This article belongs to the Special Issue Internet of Things, Embedded Solutions, and Edge Intelligence for Smart Health)
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19 pages, 28891 KiB  
Article
Analysis of Magnetotherapy Device-Induced Fields Using Cylindrical Human Body Model
by Mario Cvetković and Bruno Sučić
Electronics 2024, 13(5), 849; https://doi.org/10.3390/electronics13050849 - 23 Feb 2024
Viewed by 606
Abstract
This paper deals with the analysis of induced current density and the induced electric field in the body of a human exposed to the magnetic field of a magnetotherapy device. As the displacement currents at extremely low frequencies can be neglected, the biological [...] Read more.
This paper deals with the analysis of induced current density and the induced electric field in the body of a human exposed to the magnetic field of a magnetotherapy device. As the displacement currents at extremely low frequencies can be neglected, the biological tissues can thus be considered a weakly conducting medium, facilitating the use of a quasi-static eddy current approximation. The formulation is based on the surface integral equation for the unknown surface charges, whose numerical solution is obtained using the method of moments technique. A simplified model of the human body is utilized to examine various scenarios during the magnetotherapy procedure. The numerical results for the induced current density and the induced electric field are obtained using the proposed model. The analyses of various stimulating coil parameters, human body model parameters, and a displacement of the magnetotherapy coil were carried out to assess their effects on the induced current density. The results suggest that selection of the stimulating coil should be matched based on the size of the human body, but also that the position and orientation of the coil with respect to the body surface will result in different distributions of the induced fields. The results of this study could be useful for medical professionals by showing the importance of various magnetotherapy coil parameters for preparation of various treatment scenarios. Full article
(This article belongs to the Special Issue Internet of Things, Embedded Solutions, and Edge Intelligence for Smart Health)
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13 pages, 540 KiB  
Article
Evaluation of a Telemergency Service for Older People Living at Home: A Cross-Sectional Study
by Elena Casabona, Sara Campagna, Lorena Charrier, Dante Viotti, Angela Castello, Paola Di Giulio and Valerio Dimonte
Electronics 2023, 12(23), 4786; https://doi.org/10.3390/electronics12234786 - 26 Nov 2023
Viewed by 545
Abstract
Personal Emergency Response Systems (PERSs) are fall-detection devices supporting users in any situation. No previous studies have investigated the differences in events and the use of PERS between users financially supported by public authorities (public users) and those who privately afford the PERS [...] Read more.
Personal Emergency Response Systems (PERSs) are fall-detection devices supporting users in any situation. No previous studies have investigated the differences in events and the use of PERS between users financially supported by public authorities (public users) and those who privately afford the PERS cost (private users). More than two years of data collected by the Telemergency Operation Centre (TOC) were downloaded. All users who sent at least one real alert to request support were included. No differences were found for falls (37, 16.7% vs. 95, 13.4%) and medical problems (46, 20.7% vs. 122, 17.2%). The dispatch of an ambulance was necessary for all medical problems, while for falls, this was only in half of cases. Public users significantly asked more for service demand, while private users asked for support calls. The TOC staff directly managed most of the service demands (398, 97.3%) and support calls. PERS could be a valid instrument for promoting independent living and helping manage chronic conditions in older adults. The results suggest that PERSs might improve in-home care services, facilitating the connection to in-home services. Full article
(This article belongs to the Special Issue Internet of Things, Embedded Solutions, and Edge Intelligence for Smart Health)
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13 pages, 4994 KiB  
Article
Feature-Based Gait Pattern Modeling on a Treadmill
by Woo-Chul Shin, Min-Jung Kim, Ji-Hun Han, Hyun-Sang Cho and Youn-Sik Hong
Electronics 2023, 12(20), 4201; https://doi.org/10.3390/electronics12204201 - 10 Oct 2023
Viewed by 981
Abstract
In this paper, we present a method of gait analysis on a treadmill based on pressure distribution. We aimed to model the gait patterns of a subject walking at a constant speed on a treadmill based on differences in current consumption. The changes [...] Read more.
In this paper, we present a method of gait analysis on a treadmill based on pressure distribution. We aimed to model the gait patterns of a subject walking at a constant speed on a treadmill based on differences in current consumption. The changes in current consumption were converted into pressure distribution curves, and then specific features were extracted. The extracted features were used to model the walking pattern on a treadmill. To verify the validity of our proposed feature-based gait pattern modeling, we conducted experiments by gender, age, BMI (body mass index), and step-to-step symmetry. The experimental results showed that the heavier the subject, the higher the value of each feature. In particular, our feature point-based gait modeling provides an index that can help determine whether a subject’s gait is abnormal, depending on the difference between the features. Full article
(This article belongs to the Special Issue Internet of Things, Embedded Solutions, and Edge Intelligence for Smart Health)
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