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Special Issue "Intelligent Systems for Clinical Care and Remote Patient Monitoring"

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

Deadline for manuscript submissions: 10 October 2022 | Viewed by 9158
Please feel free to contact Guest Editors or Special Issue Editor ([email protected]) for any queries.

Special Issue Editors

Dr. Giovanna Sannino
E-Mail Website
Guest Editor
Institute of High Performance Computing and Networking (ICAR) of National Research Council (CNR), 80131 Naples, Italy
Interests: eHealth; mobile health; signal processing; pattern recognition; biomechanical and physiological parameter extraction and analysis; statistical analysis; machine learning/Artificial Intelligence techniques for eHealth applications; ICT-based intelligent solutions for chronic disease (cardiovascular diseases); wearable devices (ECG sensors, accelerometer sensors, etc.)
Dr. Antonio Celesti
E-Mail Website1 Website2
Guest Editor
MIFT Department, University of Messina, Viale F. Stagno d'Alcontres, 31 98166 Messina, Italy
Interests: distributed systems; cloud computing; edge computing; Internet of Things (IoT); machine learning; assistive technology; eHealth
Special Issues, Collections and Topics in MDPI journals
Dr. Ivanoe De Falco
E-Mail Website
Guest Editor
Institute of High Performance Computing and Networking of National Research Council (ICAR-CNR), 80131 Naples, Italy
Interests: artificial intelligence; machine learning; soft computing; computational intelligence; parallel and distributed computing; explainable artificial intelligence; AI/ML applications to eHealth and mobile health; pattern recognition; signal processing; optimization; classification; regression; time series forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The year 2020 was definitely like no other. COVID-19 shut down the world, and its spread and resulting impact led to a global crisis of unprecedented reach and proportion. This virus has impacted all our lives and forced us to adapt—especially when it comes to access to healthcare services.

Recent innovations, such as the use of Artificial Intelligence (AI) in healthcare, but also other important technologies, have resulted in better communication and collaboration between medical professionals, improved virtual patient care, and generated the diffusion of a multitude of medical devices that are saving lives every day.

These tools will see an even more massive boost in 2021.

For these reasons, this Special Issue focuses attention on new technologies, tools, and methodologies for the development of new intelligent systems for clinical care and remote patient monitoring. Among them, we can cite micro/nano/cyberphysical systems; AI and machine learning techniques; early pathology detection technologies; e-health, mHealth, telemedicine, and digital solutions; and augmented reality for remote healthcare treatments.

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

  • Fast, cost-effective, and easily deployable sampling, screening, diagnostic, and prognostic systems, including new methods for screening, using for example AI, ML, or other advanced solutions;
  • Low-cost sensors, smart wearable devices, and robotics/AI for telemedicine, telerehabilitation, telepresence, and continuous remote monitoring of patient parameters;
  • Innovative data-driven services, algorithms, and tools for data analysis, data management, and data fusion from various relevant privately held and/or publicly available sources;
  • Services for privacy, data protection, and anonymity in the use of mobile healthcare and prevention applications.

All these will help doctors and citizens in the management of both COVID and, especially, non-COVID-19-related pathologies.

Dr. Giovanna Sannino
Dr. Antonio Celesti
Dr. Ivanoe De Falco
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 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

  • telemedicine
  • smart sensors
  • artificial intelligence
  • remote monitoring
  • data analysis
  • data fusion
  • data protection
  • mHealth
  • screening applications
  • healthcare prevention

Published Papers (8 papers)

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Research

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Article
Healing Hands: The Tactile Internet in Future Tele-Healthcare
Sensors 2022, 22(4), 1404; https://doi.org/10.3390/s22041404 - 11 Feb 2022
Cited by 1 | Viewed by 841
Abstract
In the early 2020s, the coronavirus pandemic brought the notion of remotely connected care to the general population across the globe. Oftentimes, the timely provisioning of access to and the implementation of affordable care are drivers behind tele-healthcare initiatives. Tele-healthcare has already garnered [...] Read more.
In the early 2020s, the coronavirus pandemic brought the notion of remotely connected care to the general population across the globe. Oftentimes, the timely provisioning of access to and the implementation of affordable care are drivers behind tele-healthcare initiatives. Tele-healthcare has already garnered significant momentum in research and implementations in the years preceding the worldwide challenge of 2020, supported by the emerging capabilities of communication networks. The Tactile Internet (TI) with human-in-the-loop is one of those developments, leading to the democratization of skills and expertise that will significantly impact the long-term developments of the provisioning of care. However, significant challenges remain that require today’s communication networks to adapt to support the ultra-low latency required. The resulting latency challenge necessitates trans-disciplinary research efforts combining psychophysiological as well as technological solutions to achieve one millisecond and below round-trip times. The objective of this paper is to provide an overview of the benefits enabled by solving this network latency reduction challenge by employing state-of-the-art Time-Sensitive Networking (TSN) devices in a testbed, realizing the service differentiation required for the multi-modal human-machine interface. With completely new types of services and use cases resulting from the TI, we describe the potential impacts on remote surgery and remote rehabilitation as examples, with a focus on the future of tele-healthcare in rural settings. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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Article
Mixed Reality-Based Interaction between Human and Virtual Cat for Mental Stress Management
Sensors 2022, 22(3), 1159; https://doi.org/10.3390/s22031159 - 03 Feb 2022
Viewed by 759
Abstract
Human–animal interaction (HAI) has been observed to effectively reduce stress and induce positive emotions owing to the process of directly petting and interacting with animals. Interaction with virtual animals has recently emerged as an alternative due to the limitations in general physical interactions, [...] Read more.
Human–animal interaction (HAI) has been observed to effectively reduce stress and induce positive emotions owing to the process of directly petting and interacting with animals. Interaction with virtual animals has recently emerged as an alternative due to the limitations in general physical interactions, both due to the COVID-19 pandemic and, more generally, due to the difficulties involved in providing adequate care for animals. This study proposes mixed reality (MR)-based human–animal interaction content along with presenting the experimental verification of its effect on the reduction of mental stress. A mental arithmetic task was employed to induce acute mental stress, which was followed by either MR content, in which a participant interacted with virtual animals via gestures and voice commands, or a slide show of animal images. During the experiment, an electrocardiogram (ECG) was continuously recorded with a patch-type, wireless ECG sensor on the chest of the subject, and their psychological state was evaluated with the help of questionnaires after each task. The findings of the study demonstrate that the MR-based interaction with virtual animals significantly reduces mental stress and induces positive emotions. We expect that this study could provide a basis for the widespread use of MR-based content in the field of mental health. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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Article
Mental Health Intent Recognition for Arabic-Speaking Patients Using the Mini International Neuropsychiatric Interview (MINI) and BERT Model
Sensors 2022, 22(3), 846; https://doi.org/10.3390/s22030846 - 23 Jan 2022
Viewed by 1201
Abstract
For many years, mental health has been hidden behind a veil of shame and prejudice. In 2017, studies claimed that 10.7% of the global population suffered from mental health disorders. Recently, people started seeking relaxing treatment through technology, which enhanced and expanded mental [...] Read more.
For many years, mental health has been hidden behind a veil of shame and prejudice. In 2017, studies claimed that 10.7% of the global population suffered from mental health disorders. Recently, people started seeking relaxing treatment through technology, which enhanced and expanded mental health care, especially during the COVID-19 pandemic, where the use of mental health forums, websites, and applications has increased by 95%. However, these solutions still have many limits, as existing mental health technologies are not meant for everyone. In this work, an up-to-date literature review on state-of-the-art of mental health and healthcare solutions is provided. Then, we focus on Arab-speaking patients and propose an intelligent tool for mental health intent recognition. The proposed system uses the concepts of intent recognition to make mental health diagnoses based on a bidirectional encoder representations from transformers (BERT) model and the International Neuropsychiatric Interview (MINI). Experiments are conducted using a dataset collected at the Military Hospital of Tunis in Tunisia. Results show excellent performance of the proposed system (the accuracy is over 92%, the precision, recall, and F1 scores are over 94%) in mental health patient diagnosis for five aspects (depression, suicidality, panic disorder, social phobia, and adjustment disorder). In addition, the tool was tested and evaluated by medical staff at the Military Hospital of Tunis, who found it very interesting to help decision-making and prioritizing patient appointment scheduling, especially with a high number of treated patients every day. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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Article
Remote Arrhythmia Detection for Eldercare in Malaysia
Sensors 2021, 21(24), 8197; https://doi.org/10.3390/s21248197 - 08 Dec 2021
Viewed by 517
Abstract
Cardiovascular disease continues to be one of the most prevalent medical conditions in modern society, especially among elderly citizens. As the leading cause of deaths worldwide, further improvements to the early detection and prevention of these cardiovascular diseases is of the utmost importance [...] Read more.
Cardiovascular disease continues to be one of the most prevalent medical conditions in modern society, especially among elderly citizens. As the leading cause of deaths worldwide, further improvements to the early detection and prevention of these cardiovascular diseases is of the utmost importance for reducing the death toll. In particular, the remote and continuous monitoring of vital signs such as electrocardiograms are critical for improving the detection rates and speed of abnormalities while improving accessibility for elderly individuals. In this paper, we consider the design and deployment characteristics of a remote patient monitoring system for arrhythmia detection in elderly individuals. Thus, we developed a scalable system architecture to support remote streaming of ECG signals at near real-time. Additionally, a two-phase classification scheme is proposed to improve the performance of existing ECG classification algorithms. A prototype of the system was deployed at the Sarawak General Hospital, remotely collecting data from 27 unique patients. Evaluations indicate that the two-phase classification scheme improves algorithm performance when applied to the MIT-BIH Arrhythmia Database and the remotely collected single-lead ECG recordings. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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Review

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Review
Are Smart Homes Adequate for Older Adults with Dementia?
Sensors 2022, 22(11), 4254; https://doi.org/10.3390/s22114254 - 02 Jun 2022
Viewed by 486
Abstract
Smart home technologies can enable older adults, including those with dementia, to live more independently in their homes for a longer time. Activity recognition, in combination with anomaly detection, has shown the potential to recognise users’ daily activities and detect deviations. However, activity [...] Read more.
Smart home technologies can enable older adults, including those with dementia, to live more independently in their homes for a longer time. Activity recognition, in combination with anomaly detection, has shown the potential to recognise users’ daily activities and detect deviations. However, activity recognition and anomaly detection are not sufficient, as they lack the capacity to capture the progression of patients’ habits across the different stages of dementia. To achieve this, smart homes should be enabled to recognise patients’ habits and changes in habits, including the loss of some habits. In this study, we first present an overview of the stages that characterise dementia, alongside real-world personas that depict users’ behaviours at each stage. Then, we survey the state of the art on activity recognition in smart homes for older adults with dementia, including the literature that combines activity recognition and anomaly detection. We categorise the literature based on goals, stages of dementia, and targeted users. Finally, we justify the necessity for habit recognition in smart homes for older adults with dementia, and we discuss the research challenges related to its implementation. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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Review
Quantification of Movement in Stroke Patients under Free Living Conditions Using Wearable Sensors: A Systematic Review
Sensors 2022, 22(3), 1050; https://doi.org/10.3390/s22031050 - 28 Jan 2022
Viewed by 1190
Abstract
Stroke is a main cause of long-term disability worldwide, placing a large burden on individuals and health care systems. Wearable technology can potentially objectively assess and monitor patients outside clinical environments, enabling a more detailed evaluation of their impairment and allowing individualization of [...] Read more.
Stroke is a main cause of long-term disability worldwide, placing a large burden on individuals and health care systems. Wearable technology can potentially objectively assess and monitor patients outside clinical environments, enabling a more detailed evaluation of their impairment and allowing individualization of rehabilitation therapies. The aim of this review is to provide an overview of setups used in literature to measure movement of stroke patients under free living conditions using wearable sensors, and to evaluate the relation between such sensor-based outcomes and the level of functioning as assessed by existing clinical evaluation methods. After a systematic search we included 32 articles, totaling 1076 stroke patients from acute to chronic phases and 236 healthy controls. We summarized the results by type and location of sensors, and by sensor-based outcome measures and their relation with existing clinical evaluation tools. We conclude that sensor-based measures of movement provide additional information in relation to clinical evaluation tools assessing motor functioning and both are needed to gain better insight in patient behavior and recovery. However, there is a strong need for standardization and consensus, regarding clinical assessments, but also regarding the use of specific algorithms and metrics for unsupervised measurements during daily life. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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Review
Development Technologies for the Monitoring of Six-Minute Walk Test: A Systematic Review
Sensors 2022, 22(2), 581; https://doi.org/10.3390/s22020581 - 12 Jan 2022
Cited by 2 | Viewed by 802
Abstract
In the pandemic time, the monitoring of the progression of some diseases is affected and rehabilitation is more complicated. Remote monitoring may help solve this problem using mobile devices that embed low-cost sensors, which can help measure different physical parameters. Many tests can [...] Read more.
In the pandemic time, the monitoring of the progression of some diseases is affected and rehabilitation is more complicated. Remote monitoring may help solve this problem using mobile devices that embed low-cost sensors, which can help measure different physical parameters. Many tests can be applied remotely, one of which is the six-minute walk test (6MWT). The 6MWT is a sub-maximal exercise test that assesses aerobic capacity and endurance, allowing early detection of emerging medical conditions with changes. This paper presents a systematic review of the use of sensors to measure the different physical parameters during the performance of 6MWT, focusing on various diseases, sensors, and implemented methodologies. It was performed with the PRISMA methodology, where the search was conducted in different databases, including IEEE Xplore, ACM Digital Library, ScienceDirect, and PubMed Central. After filtering the papers related to 6MWT and sensors, we selected 31 papers that were analyzed in more detail. Our analysis discovered that the measurements of 6MWT are primarily performed with inertial and magnetic sensors. Likewise, most research studies related to this test focus on multiple sclerosis and pulmonary diseases. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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Other

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Systematic Review
Machine Learning and Smart Devices for Diabetes Management: Systematic Review
Sensors 2022, 22(5), 1843; https://doi.org/10.3390/s22051843 - 25 Feb 2022
Cited by 2 | Viewed by 1661
Abstract
(1) Background: The use of smart devices to better manage diabetes has increased significantly in recent years. These technologies have been introduced in order to make life easier for patients with diabetes by allowing better control of the stability of blood sugar levels [...] Read more.
(1) Background: The use of smart devices to better manage diabetes has increased significantly in recent years. These technologies have been introduced in order to make life easier for patients with diabetes by allowing better control of the stability of blood sugar levels and anticipating the occurrence of dangerous events (hypo/hyperglycemia), etc. That being said, the main objectives of the self-management of diabetes is to improve the lifestyle and life quality of patients with diabetes; (2) Methods: We performed a systematic review based on articles that focus on the use of smart devices for the monitoring and better management of diabetes. The search was focused on keywords related to the topic, such as “Diabetes”, “Technology”, “Self-management”, “Artificial Intelligence”, etc. This was performed using databases, such as Scopus, Google Scholar, and PubMed; (3) Results: A total of 89 studies, published between 2011 and 2021, were included. The majority of the selected research aims to solve a diabetes management problem (e.g., blood glucose prediction, early detection of risk events, and the automatic adjustment of insulin doses, etc.). In these studies, wearable devices were used in combination with artificial intelligence (AI) techniques; (4) Conclusions: Wearable devices have attracted a great deal of scientific interest in the field of healthcare for people with chronic conditions, such as diabetes. They are capable of assisting in the management of diabetes, as well as preventing complications associated with this condition. Furthermore, the usage of these devices has improved illness management and quality of life. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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