New Technologies and Smart Solutions in IoT-Based Personalized Healthcare Applications

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 10686

Special Issue Editor


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Guest Editor
Departament of Innovation Engineering, University of Salento, 73100 Lecce, Italy
Interests: design and testing of IoT-based electronic systems; smart remote control of facilities; electronic systems for automation and automotive; energy harvesting systems for sensors nodes; wearable devices for health monitoring; new materials and advanced sensors
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Special Issue Information

Dear Colleagues,

Health systems are undergoing a real technological revolution, radically changing how medical services are provided, thanks to the wide employment of the Internet of Things (IoT) platforms supporting advanced monitoring services and intelligent inference systems. Internet of Medical Things (IoMT) platforms are opening new frontiers in in-home medical care, allowing integrated, capillary and precise monitoring of patients’ conditions; reduced management costs; better diagnosis; earlier prevention; continuous tracking; and more rapid intervention. These platforms aim to provide a secure connection to source devices to collect data, process them to extract helpful information, and securely store them. Several technologies can be exploited and combined in IoT (Internet of Things) platforms, such as advanced sensing, wearable devices, cloud and fog computing, as well as predictive and inference techniques. Wearable devices are powerful tools for supporting IoT systems because of their sensing, processing, and communication capabilities. Particularly, they allow the detection of patients’ vital parameters (e.g., heart rate (HR), oxygen saturation (SpO2), body temperature, blood pressure (BP), etc.) through advanced sensors positioned in direct contact with the skin, providing advantages such as continuous monitoring, reduced health costs and hospitalization times and the more straightforward prescription of preventive care, which is crucial for elderly people and infants. Moreover, the development of advanced sensing devices for monitoring biophysical and environmental parameters featured by small sizes, reduced power consumption, non-invasiveness, and flexibility is fundamental for developing the next generation of wearable devices.

Summing up, this Special Issue titled “New Technologies and Smart Solutions in IoT-Based Personalized Healthcare Applications” aims to bring together innovative developments and synergies in the following topics (not an exhaustive list):

  • Medical IoT platforms for remote patient monitoring;
  • Privacy and security of individuals’ medical data against unauthorized access;
  • Healthcare data management platforms of healthcare IoT devices;
  • Blockchain for healthcare data management;
  • IoT platforms for COVID-19 prevention and control
  • Fog-computing-based IoT for health monitoring systems;
  • Machine learning algorithms for medical diagnosis;
  • Electronic wearable solutions for IoT-based health monitoring applications (glucose, heart rate, depression and mood, Parkinson’s disease, etc.)
  • Wearable systems for biophysical parameters detection: electronic issues;
  • Wearable devices for assisting people with physical disabilities, active living, and rehabilitation;
  • Ingestible and implantable sensors for collecting data from inside the human body;
  • Electronic wearable solutions for smart homes and workplaces;
  • Low-power electronic solutions for signal acquisition/processing from wearable sensors;·       
  • Embedded solutions and platforms for data processing: firmware issues and applications;·        
  • Software development for wearable sensors and body sensor networks. 

Finally, I would like to thank Dr. Fazio Roberto and Prof. Bassam Al-Naami for their valuable work in assisting me with this special issue.

Prof. Dr. Paolo Visconti
Guest Editor

Manuscript Submission Information

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Keywords

  • IoT
  • healthcare applications

Published Papers (3 papers)

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Research

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27 pages, 5503 KiB  
Article
A BLE-Connected Piezoresistive and Inertial Chest Band for Remote Monitoring of the Respiratory Activity by an Android Application: Hardware Design and Software Optimization
by Roberto De Fazio, Massimo De Vittorio and Paolo Visconti
Future Internet 2022, 14(6), 183; https://doi.org/10.3390/fi14060183 - 11 Jun 2022
Cited by 6 | Viewed by 2487
Abstract
Breathing is essential for human life. Issues related to respiration can be an indicator of problems related to the cardiorespiratory system; thus, accurate breathing monitoring is fundamental for establishing the patient’s condition. This paper presents a ready-to-use and discreet chest band for monitoring [...] Read more.
Breathing is essential for human life. Issues related to respiration can be an indicator of problems related to the cardiorespiratory system; thus, accurate breathing monitoring is fundamental for establishing the patient’s condition. This paper presents a ready-to-use and discreet chest band for monitoring the respiratory parameters based on the piezoresistive transduction mechanism. In detail, it relies on a strain sensor realized with a pressure-sensitive fabric (EeonTex LTT-SLPA-20K) for monitoring the chest movements induced by respiration. In addition, the band includes an Inertial Measurement Unit (IMU), which is used to remove the motion artefacts from the acquired signal, thereby improving the measurement reliability. Moreover, the band comprises a low-power conditioning and acquisition section that processes the signal from sensors, providing a reliable measurement of the respiration rate (RR), in addition to other breathing parameters, such as inhalation (TI) and exhalation (TE) times, inhalation-to-exhalation ratio (IER), and flow rate (V). The device wirelessly transmits the extracted parameters to a host device, where a custom mobile application displays them. Different test campaigns were carried out to evaluate the performance of the designed chest band in measuring the RR, by comparing the measurements provided by the chest band with those obtained by breath count. In detail, six users, of different genders, ages, and physical constitutions, were involved in the tests. The obtained results demonstrated the effectiveness of the proposed approach in detecting the RR. The achieved performance was in line with that of other RR monitoring systems based on piezoresistive textiles, but which use more powerful acquisition systems or have low wearability. In particular, the inertia-assisted piezoresistive chest band obtained a Pearson correlation coefficient with respect to the measurements based on breath count of 0.96 when the user was seated. Finally, Bland–Altman analysis demonstrated that the developed system obtained 0.68 Breaths Per Minute (BrPM) mean difference (MD), and Limits of Agreement (LoAs) of +3.20 and −1.75 BrPM when the user was seated. Full article
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20 pages, 2972 KiB  
Article
Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyperparameter-Optimized Neural Networks
by Roseline Oluwaseun Ogundokun, Sanjay Misra, Mychal Douglas, Robertas Damaševičius and Rytis Maskeliūnas
Future Internet 2022, 14(5), 153; https://doi.org/10.3390/fi14050153 - 18 May 2022
Cited by 54 | Viewed by 5150
Abstract
In today’s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data [...] Read more.
In today’s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches. Medical IoT combines medical devices and AI applications with healthcare infrastructure to support medical diagnostics. The current state-of-the-art approach fails to diagnose breast cancer in its initial period, resulting in the death of most women. As a result, medical professionals and researchers are faced with a tremendous problem in early breast cancer detection. We propose a medical IoT-based diagnostic system that competently identifies malignant and benign people in an IoT environment to resolve the difficulty of identifying early-stage breast cancer. The artificial neural network (ANN) and convolutional neural network (CNN) with hyperparameter optimization are used for malignant vs. benign classification, while the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were utilized as baseline classifiers for comparison. Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. We employ a particle swarm optimization (PSO) feature selection approach to select more satisfactory features from the breast cancer dataset to enhance the classification performance using MLP and SVM, while grid-based search was used to find the best combination of the hyperparameters of the CNN and ANN models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used to test the proposed approach. The proposed model got a classification accuracy of 98.5% using CNN, and 99.2% using ANN. Full article
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Review

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17 pages, 677 KiB  
Review
The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research
by Magdalena Osial and Agnieszka Pregowska
Future Internet 2022, 14(12), 356; https://doi.org/10.3390/fi14120356 - 28 Nov 2022
Cited by 4 | Viewed by 2243
Abstract
The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects [...] Read more.
The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocarriers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated. Full article
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