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Advancements in the Application of Artificial Intelligence in Care and...
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Advancements in the Application of Artificial Intelligence in Care and Continuity of Care

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 15362

Special Issue Editors

Dr. Daniele Spoladore

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Guest Editor
1. Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), 23900 Lecco, Italy
2. Department of Pure and Applied Sciences, Computer Science Division, Insubria University, 21100 Varese, Italy
Interests: ontology development and engineering; decision support systems; semantic web application; applications for rehabilitation and continuity of care; smart home and environments
Special Issues, Collections and Topics in MDPI journals
Dr. Alberto Trombetta

E-Mail Website
Guest Editor
Department of Pure and Applied Sciences, Computer Science Division, Insubria University, 21100 Varese, Italy
Interests: design and implementation of secure and reliable applications in distributed; data-intensive architectures; analysis, integration and management of very large - possibly non-relational - data repositories; data modeling using Semantic Web technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Liming Luke Chen

E-Mail Website
Guest Editor
School of Computing, Ulster University, Belfast, UK
Interests: data analytics; artificial intelligence; pervasive computing; user-centred intelligent systems; smart environment; digital health and assisted living
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aging of the population is an increasing trend in several countries, which are now forced to face the socioeconomic burden of the situation on their national healthcare systems. In fact, aged population is often affected by different chronic conditions requiring specific clinical assistance and continuity of care, thus causing healthcare costs to increase considerably. Starting from the 1990s, the scientific community has tackled these issues with novel paradigms, such as personalized medicine, ambient assisted living and telehealthcare systems, able to deliver customized solutions to end users and to efficiently take on the problem of care and continuity of care.

Currently, the advancements in the field of artificial intelligence, ambient intelligence, the Internet of things, and personalized medicine can allow many end users to have access to nearly-real time assistance and to reduce the impact of chronic conditions on daily life. Moreover, since the management of chronic conditions and co-morbidities often consists in a multi-domain intervention, the ability of accessing patients’ clinical history covers a paramount importance in diagnosis and personalization of care: therefore, being able to formalize and manage health-related knowledge is fundamental.

In this context, this Special Issue encourages the submission of high-quality, original and innovative contributions regarding the application of AI, IoT and Semantic Web in the areas of personalized medicine, Continuity of Care and Ambient Assisted Living.

The list of possible topics includes (but is not limited to):

  • AI-based applications in smart homes and buildings;
  • AI-based applications exploiting wearable systems to deliver personalized care;
  • AI-based telehealthcare systems;
  • Advancements in electronic health records;
  • Knowledge-based clinical decision support systems for clinical professionals;
  • Knowledge-based clinical decision support system for patients.

Dr. Daniele Spoladore
Prof. Dr. Alberto Trombetta
Dr. Liming Luke Chen
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.

Published Papers (7 papers)

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Research

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15 pages, 4170 KiB  
Article
Personalized and Safe Soft Glove for Rehabilitation Training
by Fanye Meng, Chang Liu, Yu Li, Hao Hao, Qishen Li, Chenyi Lyu, Zimo Wang, Gang Ge, Junyi Yin, Xiaoqiang Ji and Xiao Xiao
Electronics 2023, 12(11), 2531; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics12112531 - 03 Jun 2023
Cited by 4 | Viewed by 1653
Abstract
Traditional hand rehabilitation devices present a challenge in providing personalized training that can lead to finger movements exceeding the safe range, resulting in secondary injuries. To address this issue, we introduce a soft rehabilitation training glove with the function of safety and personalization, [...] Read more.
Traditional hand rehabilitation devices present a challenge in providing personalized training that can lead to finger movements exceeding the safe range, resulting in secondary injuries. To address this issue, we introduce a soft rehabilitation training glove with the function of safety and personalization, which can allow patients to select training modes based on rehabilitation and provide real-time monitoring, as well as feedback on finger movement data. The inner glove is equipped with bending sensors to access the maximum/minimum angle of finger movement and to provide data for the safety of rehabilitation training. The outer glove contains flexible drivers, which can drive fingers for different modes of rehabilitation training. As a result, the rehabilitation glove can drive five fingers to achieve maximum extension/flexion angles of 15.65°/85.97°, 15.34°/89.53°, 16.78°/94.27°, 15.59°/88.82°, and 16.73°/88.65°, from thumb to little finger, respectively, and the rehabilitation training frequency can reach six times per minute. The safety evaluation result indicated an error within ±6.5° of the target-motion threshold. The reliability assessment yielded a high-intra-class correlation coefficient value (0.7763–0.9996). Hence, the rehabilitation glove can achieve targeted improvement in hand function while ensuring safety. Full article
(This article belongs to the Special Issue Advancements in the Application of Artificial Intelligence in Care and Continuity of Care)
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17 pages, 2348 KiB  
Article
Spike-Event X-ray Image Classification for 3D-NoC-Based Neuromorphic Pneumonia Detection
by Jiangkun Wang, Ogbodo Mark Ikechukwu, Khanh N. Dang and Abderazek Ben Abdallah
Electronics 2022, 11(24), 4157; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11244157 - 13 Dec 2022
Cited by 2 | Viewed by 1620
Abstract
The success of deep learning in extending the frontiers of artificial intelligence has accelerated the application of AI-enabled systems in addressing various challenges in different fields. In healthcare, deep learning is deployed on edge computing platforms to address security and latency challenges, even [...] Read more.
The success of deep learning in extending the frontiers of artificial intelligence has accelerated the application of AI-enabled systems in addressing various challenges in different fields. In healthcare, deep learning is deployed on edge computing platforms to address security and latency challenges, even though these platforms are often resource-constrained. Deep learning systems are based on conventional artificial neural networks, which are computationally complex, require high power, and have low energy efficiency, making them unsuitable for edge computing platforms. Since these systems are also used in critical applications such as bio-medicine, it is expedient that their reliability is considered when designing them. For biomedical applications, the spatio-temporal nature of information processing of spiking neural networks could be merged with a fault-tolerant 3-dimensional network on chip (3D-NoC) hardware to obtain an excellent multi-objective performance accuracy while maintaining low latency and low power consumption. In this work, we propose a reconfigurable 3D-NoC-based neuromorphic system for biomedical applications based on a fault-tolerant spike routing scheme. The performance evaluation results over X-ray images for pneumonia (i.e., COVID-19) detection show that the proposed system achieves 88.43% detection accuracy over the collected test data and could be accelerated to achieve 4.6% better inference latency than the ANN-based system while consuming 32% less power. Furthermore, the proposed system maintains high accuracy for up to 30% inter-neuron communication faults with increased latency. Full article
(This article belongs to the Special Issue Advancements in the Application of Artificial Intelligence in Care and Continuity of Care)
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11 pages, 1721 KiB  
Article
GCT-UNET: U-Net Image Segmentation Model for a Small Sample of Adherent Bone Marrow Cells Based on a Gated Channel Transform Module
by Jing Qin, Tong Liu, Zumin Wang, Lu Liu and Hui Fang
Electronics 2022, 11(22), 3755; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11223755 - 16 Nov 2022
Cited by 2 | Viewed by 2335
Abstract
Pathological diagnosis is considered to be declarative and authoritative. However, reading pathology slides is a challenging task. Different parts of the section are taken and read for different purposes and with different focuses, which further adds difficulty to the pathologist’s diagnosis. In recent [...] Read more.
Pathological diagnosis is considered to be declarative and authoritative. However, reading pathology slides is a challenging task. Different parts of the section are taken and read for different purposes and with different focuses, which further adds difficulty to the pathologist’s diagnosis. In recent years, the deep neural network has made great progress in the direction of computer vision and the main approach to image segmentation is the use of convolutional neural networks, through which the spatial properties of the data are captured. Among a wide variety of different network structures, one of the more representative ones is UNET with encoder and decoder structures. The biggest advantage of traditional UNET is that it can still perform well with a small number of samples, but because the information in the feature map is lost in the downsampling process of UNET, and a large amount of spatially accurate detailed information is lost in the decoding part. This makes it difficult to complete accurate segmentation of cell images with dense numbers and high adhesion. For this reason, we propose a new network structure based on UNET, which can be used to segment cell images by aggregating the global contextual information between different channels and assigning different weights to the corresponding channels through the gated adaptive mechanism, we improve the performance of UNET in the cell segmentation task and consider the use of unsupervised segmentation methods for secondary segmentation of the predicted results of our model, and the final results obtained are tested to meet the needs of the readers. Full article
(This article belongs to the Special Issue Advancements in the Application of Artificial Intelligence in Care and Continuity of Care)
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12 pages, 564 KiB  
Article
Arrhythmia Detection Based on WGAN-GP and SE-ResNet1D
by Jing Qin, Fujie Gao, Zumin Wang, Lu Liu and Changqing Ji
Electronics 2022, 11(21), 3427; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11213427 - 23 Oct 2022
Cited by 4 | Viewed by 1876
Abstract
A WGAN-GP-based ECG signal expansion and an SE-ResNet1D-based ECG classification method are proposed to address the problem of poor modeling results due to the imbalanced sample distribution of ECG data sets. The network architectures of WGAN-GP and SE-ResNet1D are designed according to the [...] Read more.
A WGAN-GP-based ECG signal expansion and an SE-ResNet1D-based ECG classification method are proposed to address the problem of poor modeling results due to the imbalanced sample distribution of ECG data sets. The network architectures of WGAN-GP and SE-ResNet1D are designed according to the characteristics of ECG signals so that they can be better applied to the generation and classification of ECG signals. First, ECG data were generated using WGAN-GP on the MIT-BIH arrhythmia database to balance the dataset. Then, the experiments were performed using the AAMI category and inter-patient data partitioning principles, and classification experiments were performed using SE-ResNet1D on the imbalanced and balanced datasets, respectively, and compared with three networks, VGGNet, DenseNet and CNN+Bi-LSTM. The experimental results show that using WGAN-GP to balance the dataset can improve the accuracy and robustness of the model classification, and the proposed SE-ResNet1D outperforms the comparison model, with a precision of 95.80%, recall of 96.75% and an F1 measure of 96.27% on the balanced dataset. Our methods have the potential to be a useful diagnostic tool to assist cardiologists in the diagnosis of arrhythmias. Full article
(This article belongs to the Special Issue Advancements in the Application of Artificial Intelligence in Care and Continuity of Care)
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17 pages, 1366 KiB  
Article
An Ontology-Based Framework for a Telehealthcare System to Foster Healthy Nutrition and Active Lifestyle in Older Adults
by Daniele Spoladore, Vera Colombo, Sara Arlati, Atieh Mahroo, Alberto Trombetta and Marco Sacco
Electronics 2021, 10(17), 2129; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10172129 - 01 Sep 2021
Cited by 6 | Viewed by 2443
Abstract
In recent years, telehealthcare systems (TSs) have become more and more widespread, as they can contribute to promoting the continuity of care and managing chronic conditions efficiently. Most TSs and nutrition recommendation systems require much information to return appropriate suggestions. This work proposes [...] Read more.
In recent years, telehealthcare systems (TSs) have become more and more widespread, as they can contribute to promoting the continuity of care and managing chronic conditions efficiently. Most TSs and nutrition recommendation systems require much information to return appropriate suggestions. This work proposes an ontology-based TS, namely HeNuALs, aimed at fostering a healthy diet and an active lifestyle in older adults with chronic pathologies. The system is built on the formalization of users’ health conditions, which can be obtained by leveraging existing standards. This allows for modeling different pathologies via reusable knowledge, thus limiting the amount of information needed to retrieve nutritional indications from the system. HeNuALs is composed of (1) an ontological layer that stores patients and their data, food and its characteristics, and physical activity-related data, enabling the inference a series of suggestions based on the effects of foods and exercises on specific health conditions; (2) two applications that allow both the patient and the clinicians to access the data (with different permissions) stored in the ontological layer; and (3) a series of wearable sensors that can be used to monitor physical exercise (provided by the patient application) and to ensure patients’ safety. HeNuALs inferences have been validated considering two different use cases. The system revealed the ability to determine suggestions for healthy, adequate, or unhealthy dishes for a patient with respiratory disease and for a patient with diabetes mellitus. Future work foresees the extension of the HeNuALs knowledge base by exploiting automatic knowledge retrieval approaches and validation of the whole system with target users. Full article
(This article belongs to the Special Issue Advancements in the Application of Artificial Intelligence in Care and Continuity of Care)
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26 pages, 9065 KiB  
Article
An Intelligent Hybrid–Integrated System Using Speech Recognition and a 3D Display for Early Childhood Education
by Kun Xia, Xinghao Xie, Hongliang Fan and Haiyang Liu
Electronics 2021, 10(15), 1862; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10151862 - 03 Aug 2021
Cited by 9 | Viewed by 2617
Abstract
In the past few years, people’s attitudes toward early childhood education (PAUD) have undergone a complete transformation. Personalized and intelligent communication methods are highly praised, which also promotes the further focus on timely and effective human–computer interaction. Since traditional English learning that relies [...] Read more.
In the past few years, people’s attitudes toward early childhood education (PAUD) have undergone a complete transformation. Personalized and intelligent communication methods are highly praised, which also promotes the further focus on timely and effective human–computer interaction. Since traditional English learning that relies on parents consumes more time and energy and is prone to errors and omissions, this paper proposes a system based on a convolution neural network (CNN) and automatic speech recognition (ASR) to achieve an integrated process of object recognition, intelligent speech interaction, and synchronization of learning records in children’s education. Compared with platforms described in the literature, not only does it shoot objects in the real-life environment to obtain English words, their pronunciation, and example sentences corresponding to them, but also it combines the technique of a three-dimensional display to help children learn abstract words. At the same time, the cloud database summarizes and tracks the learning progress by a horizontal comparison, which makes it convenient for parents to figure out the situation. The performance evaluation of image and speech recognition demonstrates that the overall accuracy remains above 96%. Through comprehensive experiments in different scenarios, we prove that the platform is suitable for children as an auxiliary method and cultivates their interest in learning English. Full article
(This article belongs to the Special Issue Advancements in the Application of Artificial Intelligence in Care and Continuity of Care)
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Review

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16 pages, 1033 KiB  
Review
Ambient Assisted Working Solutions for the Ageing Workforce: A Literature Review
by Daniele Spoladore and Alberto Trombetta
Electronics 2023, 12(1), 101; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics12010101 - 27 Dec 2022
Cited by 3 | Viewed by 1484
Abstract
The increase in older workers in industrialized countries has become evident in the past two decades. The need to support the ageing workforce to effectively perform their tasks has resulted in Ambient Assisted Working (AAW), consisting of developing “smart” systems that can adapt [...] Read more.
The increase in older workers in industrialized countries has become evident in the past two decades. The need to support the ageing workforce to effectively perform their tasks has resulted in Ambient Assisted Working (AAW), consisting of developing “smart” systems that can adapt themselves to workers’ needs by exploiting ambient intelligence (AmI) solutions. In AAW, AmI provides flexible workplace adaptations for a wide range of older workers (including persons characterized by chronic conditions and disabilities), while ensuring the ageing workforce’s safety and comfort within the workplace. This work proposes a systematic literature review with the aim of identifying trends among existing AAW solutions specifically designed for older workers. The review adopted the PRISMA methodology, focusing on journal articles and surveying more than 1500 works. The review underlined an absence of articles completely devoted to this research question. Nonetheless, by extending the research question to existing AmI solutions for workers that could potentially be able to support older workers in performing their working activities, it was possible to draw some considerations on the adoption of AmI for the ageing workforce. Among them, the review identified the different types of supporting AmI solutions provided to AAW, which technologies were adopted, and which workplaces were investigated the most. Finally, this work leveraged the findings of the review process to sketch some future research directions for AAW as a discipline. Full article
(This article belongs to the Special Issue Advancements in the Application of Artificial Intelligence in Care and Continuity of Care)
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