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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: closed (31 October 2022) | Viewed by 61646
Please feel free to contact Guest Editors or Special Issue Editor (charlie.cai@mdpi.com) for any queries.

Special Issue Editors


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Guest Editor
Institute for High-Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), 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.)
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Mathematics, Computer Science, Physics and Hearth Sciences (MIFT), University of Messina, 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

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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

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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.

Keywords

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

Published Papers (19 papers)

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Editorial

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5 pages, 180 KiB  
Editorial
Special Issue: “Intelligent Systems for Clinical Care and Remote Patient Monitoring”
by Giovanna Sannino, Antonio Celesti and Ivanoe De Falco
Sensors 2023, 23(18), 7993; https://doi.org/10.3390/s23187993 - 20 Sep 2023
Viewed by 557
Abstract
The year 2020 was definitely like no other [...] Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)

Research

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18 pages, 5193 KiB  
Article
Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images
by Esraa Hassan, Samir Elmougy, Mai R. Ibraheem, M. Shamim Hossain, Khalid AlMutib, Ahmed Ghoneim, Salman A. AlQahtani and Fatma M. Talaat
Sensors 2023, 23(12), 5393; https://doi.org/10.3390/s23125393 - 07 Jun 2023
Cited by 9 | Viewed by 2230
Abstract
Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response [...] Read more.
Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therapeutic effectiveness in various fields of clinical practices, including primary eye diseases and systemic diseases such as diabetes. Therefore, precise diagnosis, classification, and automated image analysis models are crucial. In this paper, we propose an enhanced optical coherence tomography (EOCT) model to classify retinal OCT based on modified ResNet (50) and random forest algorithms, which are used in the proposed study’s training strategy to enhance performance. The Adam optimizer is applied during the training process to increase the efficiency of the ResNet (50) model compared with the common pre-trained models, such as spatial separable convolutions and visual geometry group (VGG) (16). The experimentation results show that the sensitivity, specificity, precision, negative predictive value, false discovery rate, false negative rate accuracy, and Matthew’s correlation coefficient are 0.9836, 0.9615, 0.9740, 0.9756, 0.0385, 0.0260, 0.0164, 0.9747, 0.9788, and 0.9474, respectively. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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18 pages, 3893 KiB  
Article
A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer
by Valentina Brancato, Nadia Brancati, Giusy Esposito, Massimo La Rosa, Carlo Cavaliere, Ciro Allarà, Valeria Romeo, Giuseppe De Pietro, Marco Salvatore, Marco Aiello and Mara Sangiovanni
Sensors 2023, 23(3), 1552; https://doi.org/10.3390/s23031552 - 31 Jan 2023
Cited by 3 | Viewed by 2396
Abstract
Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen [...] Read more.
Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER− classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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13 pages, 308 KiB  
Article
Human Activity Recognition with an HMM-Based Generative Model
by Narges Manouchehri and Nizar Bouguila
Sensors 2023, 23(3), 1390; https://doi.org/10.3390/s23031390 - 26 Jan 2023
Cited by 4 | Viewed by 2234
Abstract
Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily [...] Read more.
Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this work, we propose unsupervised, scaled, Dirichlet-based hidden Markov models to analyze human activities. Our motivation is that human activities have sequential patterns and hidden Markov models (HMMs) are some of the strongest statistical models used for modeling data with continuous flow. In this paper, we assume that emission probabilities in HMM follow a bounded–scaled Dirichlet distribution, which is a proper choice in modeling proportional data. To learn our model, we applied the variational inference approach. We used a publicly available dataset to evaluate the performance of our proposed model. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
26 pages, 978 KiB  
Article
A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem
by Stefano Silvestri, Shareeful Islam, Spyridon Papastergiou, Christos Tzagkarakis and Mario Ciampi
Sensors 2023, 23(2), 651; https://doi.org/10.3390/s23020651 - 06 Jan 2023
Cited by 13 | Viewed by 5894
Abstract
Digitization in healthcare systems, with the wid adoption of Electronic Health Records, connected medical devices, software and systems providing efficient healthcare service delivery and management. On the other hand, the use of these systems has significantly increased cyber threats in the healthcare sector. [...] Read more.
Digitization in healthcare systems, with the wid adoption of Electronic Health Records, connected medical devices, software and systems providing efficient healthcare service delivery and management. On the other hand, the use of these systems has significantly increased cyber threats in the healthcare sector. Vulnerabilities in the existing and legacy systems are one of the key causes for the threats and related risks. Understanding and addressing the threats from the connected medical devices and other parts of the ICT health infrastructure are of paramount importance for ensuring security within the overall healthcare ecosystem. Threat and vulnerability analysis provides an effective way to lower the impact of risks relating to the existing vulnerabilities. However, this is a challenging task due to the availability of massive data which makes it difficult to identify potential patterns of security issues. This paper contributes towards an effective threats and vulnerabilities analysis by adopting Machine Learning models, such as the BERT neural language model and XGBoost, to extract updated information from the Natural Language documents largely available on the web, evaluating at the same time the level of the identified threats and vulnerabilities that can impact on the healthcare system, providing the required information for the most appropriate management of the risk. Experiments were performed based on CS news extracted from the Hacker News website and on Common Vulnerabilities and Exposures (CVE) vulnerability reports. The results demonstrate the effectiveness of the proposed approach, which provides a realistic manner to assess the threats and vulnerabilities from Natural Language texts, allowing adopting it in real-world Healthcare ecosystems. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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12 pages, 9294 KiB  
Article
A Deep-Learning-Based Collaborative Edge–Cloud Telemedicine System for Retinopathy of Prematurity
by Zeliang Luo, Xiaoxuan Ding, Ning Hou and Jiafu Wan
Sensors 2023, 23(1), 276; https://doi.org/10.3390/s23010276 - 27 Dec 2022
Cited by 4 | Viewed by 1877
Abstract
Retinopathy of prematurity is an ophthalmic disease with a very high blindness rate. With its increasing incidence year by year, its timely diagnosis and treatment are of great significance. Due to the lack of timely and effective fundus screening for premature infants in [...] Read more.
Retinopathy of prematurity is an ophthalmic disease with a very high blindness rate. With its increasing incidence year by year, its timely diagnosis and treatment are of great significance. Due to the lack of timely and effective fundus screening for premature infants in remote areas, leading to an aggravation of the disease and even blindness, in this paper, a deep learning-based collaborative edge-cloud telemedicine system is proposed to mitigate this issue. In the proposed system, deep learning algorithms are mainly used for classification of processed images. Our algorithm is based on ResNet101 and uses undersampling and resampling to improve the data imbalance problem in the field of medical image processing. Artificial intelligence algorithms are combined with a collaborative edge–cloud architecture to implement a comprehensive telemedicine system to realize timely screening and diagnosis of retinopathy of prematurity in remote areas with shortages or a complete lack of expert medical staff. Finally, the algorithm is successfully embedded in a mobile terminal device and deployed through the support of a core hospital of Guangdong Province. The results show that we achieved 75% ACC and 60% AUC. This research is of great significance for the development of telemedicine systems and aims to mitigate the lack of medical resources and their uneven distribution in rural areas. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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32 pages, 4393 KiB  
Article
Usability of the REHOME Solution for the Telerehabilitation in Neurological Diseases: Preliminary Results on Motor and Cognitive Platforms
by Claudia Ferraris, Irene Ronga, Roberto Pratola, Guido Coppo, Tea Bosso, Sara Falco, Gianluca Amprimo, Giuseppe Pettiti, Simone Lo Priore, Lorenzo Priano, Alessandro Mauro and Debora Desideri
Sensors 2022, 22(23), 9467; https://doi.org/10.3390/s22239467 - 04 Dec 2022
Cited by 8 | Viewed by 2311
Abstract
The progressive aging of the population and the consequent growth of individuals with neurological diseases and related chronic disabilities, will lead to a general increase in the costs and resources needed to ensure treatment and care services. In this scenario, telemedicine and e-health [...] Read more.
The progressive aging of the population and the consequent growth of individuals with neurological diseases and related chronic disabilities, will lead to a general increase in the costs and resources needed to ensure treatment and care services. In this scenario, telemedicine and e-health solutions, including remote monitoring and rehabilitation, are attracting increasing interest as tools to ensure the sustainability of the healthcare system or, at least, to support the burden for health care facilities. Technological advances in recent decades have fostered the development of dedicated and innovative Information and Communication Technology (ICT) based solutions, with the aim of complementing traditional care and treatment services through telemedicine applications that support new patient and disease management strategies. This is the background for the REHOME project, whose technological solution, presented in this paper, integrates innovative methodologies and devices for remote monitoring and rehabilitation of cognitive, motor, and sleep disorders associated with neurological diseases. One of the primary goals of the project is to meet the needs of patients and clinicians, by ensuring continuity of treatment from healthcare facilities to the patient’s home. To this end, it is important to ensure the usability of the solution by elderly and pathological individuals. Preliminary results of usability and user experience questionnaires on 70 subjects recruited in three experimental trials are presented here. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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17 pages, 725 KiB  
Article
Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment
by Martin Ubl, Tomas Koutny, Antonio Della Cioppa, Ivanoe De Falco, Ernesto Tarantino and Umberto Scafuri
Sensors 2022, 22(23), 9445; https://doi.org/10.3390/s22239445 - 02 Dec 2022
Cited by 2 | Viewed by 1430
Abstract
Diabetes is a heterogeneous group of diseases that share a common trait of elevated blood glucose levels. Insulin lowers this level by promoting glucose utilization, thus avoiding short- and long-term organ damage due to the elevated blood glucose level. A patient with diabetes [...] Read more.
Diabetes is a heterogeneous group of diseases that share a common trait of elevated blood glucose levels. Insulin lowers this level by promoting glucose utilization, thus avoiding short- and long-term organ damage due to the elevated blood glucose level. A patient with diabetes uses an insulin pump to dose insulin. The pump uses a controller to compute and dose the correct amount of insulin to keep blood glucose levels in a safe range. Insulin-pump controller development is an ongoing process aiming at fully closed-loop control. Controllers entering the market must be evaluated for safety. We propose an evaluation method that exploits an FDA-approved diabetic patient simulator. The method evaluates a Cartesian product of individual insulin-pump parameters with a fine degree of granularity. As this is a computationally intensive task, the simulator executes on a distributed cluster. We identify safe and risky combinations of insulin-pump parameter settings by applying the binomial model and decision tree to this product. As a result, we obtain a tool for insulin-pump settings and controller safety assessment. In this paper, we demonstrate the tool with the Low-Glucose Suspend and OpenAPS controllers. For average ± standard deviation, LGS and OpenAPS exhibited 1.7 ± 0.6% and 3.2 ± 1.8% of local extrema (i.e., good insulin-pump settings) out of all the entire Cartesian products, respectively. A continuous region around the best-discovered settings (i.e., the global extremum) of the insulin-pump settings spread across 4.0 ± 1.1% and 4.1 ± 1.3% of the Cartesian products, respectively. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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20 pages, 2112 KiB  
Article
Intelligent System for Detecting Deterioration of Life Satisfaction as Tool for Remote Mental-Health Monitoring
by Piotr Prokopowicz, Dariusz Mikołajewski and Emilia Mikołajewska
Sensors 2022, 22(23), 9214; https://doi.org/10.3390/s22239214 - 26 Nov 2022
Cited by 2 | Viewed by 1788
Abstract
The research described in this article is a continuation of work on a computational model of quality of life (QoL) satisfaction. In the proposed approach, overall life satisfaction is aggregated to personal life satisfaction (PLUS). The model described in the article is based [...] Read more.
The research described in this article is a continuation of work on a computational model of quality of life (QoL) satisfaction. In the proposed approach, overall life satisfaction is aggregated to personal life satisfaction (PLUS). The model described in the article is based on well-known and commonly used clinimetric scales (e.g., in psychiatry, psychology and physiotherapy). The simultaneous use of multiple scales, and the complexity of describing the quality of life with them, require complex fuzzy computational solutions. The aim of the study is twofold: (1) To develop a fuzzy model that allows for the detection of changes in life satisfaction scores (data on the influence of the COVID-19 pandemic and the war in the neighboring country were used). (2) To develop more detailed guidelines than the existing ones for further similar research on more advanced intelligent systems with computational models which allow for sensing, detecting and evaluating the psychical state. We are concerned with developing practical solutions with higher scientific and clinical utility for both small datasets and big data to use in remote patient monitoring. Two exemplary groups of specialists at risk of occupational burnout were assessed three times at different intervals in terms of life satisfaction. The aforementioned assessment was made on Polish citizens because the specific data could be gathered: before and during the pandemic and during the war in Ukraine (a neighboring country). That has a higher potential for presenting a better analysis and reflection on the practical application of the model. A research group (physiotherapists, n = 20) and a reference group (IT professionals, n = 20) participated in the study. Four clinimetric scales were used for assessment: the Perceived Stress Scale (PSS10), the Maslach Burnout Scale (MBI), the Satisfaction with Life Scale (SWLS), and the Nordic Musculoskeletal Questionnaire (NMQ). The assessment was complemented by statistical analyses and fuzzy models based on a hierarchical fuzzy system. Although several models for understanding changes in life satisfaction scores have been previously investigated, the novelty of this study lies in the use of data from three consecutive time points for the same individuals and the way they are analyzed, based on fuzzy logic. In addition, the new hierarchical structure of the model used in the study provides flexibility and transparency in the process of remotely monitoring changes in people’s mental well-being and a quick response to observed changes. The aforementioned computational approach was used for the first time. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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45 pages, 13075 KiB  
Article
A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions
by Argyro Mavrogiorgou, Athanasios Kiourtis, Spyridon Kleftakis, Konstantinos Mavrogiorgos, Nikolaos Zafeiropoulos and Dimosthenis Kyriazis
Sensors 2022, 22(22), 8615; https://doi.org/10.3390/s22228615 - 08 Nov 2022
Cited by 9 | Viewed by 2951
Abstract
Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain’s requirements for analytics-based [...] Read more.
Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain’s requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario’s requirements and datasets, for efficiently predicting the onset of a disease. To this context, seven (7) different ML algorithms (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed on top of diverse healthcare scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based on a variety of performance metrics (accuracy, recall, precision, F1-score, specificity, confusion matrix), it has been identified that a sub-set of ML algorithms are more efficient for timely predictions under specific healthcare scenarios, and that is why the envisioned ML catalogue prioritizes the ML algorithms to be used, depending on the scenarios’ nature and needed metrics. Further evaluation must be performed considering additional scenarios, involving state-of-the-art techniques (e.g., cloud deployment, federated ML) for improving the mechanism’s efficiency. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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16 pages, 2933 KiB  
Article
Healing Hands: The Tactile Internet in Future Tele-Healthcare
by Stefan Senk, Marian Ulbricht, Ievgenii Tsokalo, Justus Rischke, Shu-Chen Li, Stefanie Speidel, Giang T. Nguyen, Patrick Seeling and Frank H. P. Fitzek
Sensors 2022, 22(4), 1404; https://doi.org/10.3390/s22041404 - 11 Feb 2022
Cited by 7 | Viewed by 2770
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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15 pages, 1848 KiB  
Article
Mixed Reality-Based Interaction between Human and Virtual Cat for Mental Stress Management
by Heewon Na, Soyeon Park and Suh-Yeon Dong
Sensors 2022, 22(3), 1159; https://doi.org/10.3390/s22031159 - 03 Feb 2022
Cited by 10 | Viewed by 3449
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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24 pages, 2475 KiB  
Article
Mental Health Intent Recognition for Arabic-Speaking Patients Using the Mini International Neuropsychiatric Interview (MINI) and BERT Model
by Ridha Mezzi, Aymen Yahyaoui, Mohamed Wassim Krir, Wadii Boulila and Anis Koubaa
Sensors 2022, 22(3), 846; https://doi.org/10.3390/s22030846 - 23 Jan 2022
Cited by 7 | Viewed by 3966
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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20 pages, 4386 KiB  
Article
Remote Arrhythmia Detection for Eldercare in Malaysia
by Kevin Thomas Chew, Valliappan Raman and Patrick Hang Hui Then
Sensors 2021, 21(24), 8197; https://doi.org/10.3390/s21248197 - 08 Dec 2021
Cited by 1 | Viewed by 1829
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

Jump to: Editorial, Research, Other

18 pages, 342 KiB  
Review
Are Smart Homes Adequate for Older Adults with Dementia?
by Gibson Chimamiwa, Alberto Giaretta, Marjan Alirezaie, Federico Pecora and Amy Loutfi
Sensors 2022, 22(11), 4254; https://doi.org/10.3390/s22114254 - 02 Jun 2022
Cited by 7 | Viewed by 3850
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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45 pages, 3862 KiB  
Review
Quantification of Movement in Stroke Patients under Free Living Conditions Using Wearable Sensors: A Systematic Review
by Mariano Bernaldo de Quirós, E.H. Douma, Inge van den Akker-Scheek, Claudine J. C. Lamoth and Natasha M. Maurits
Sensors 2022, 22(3), 1050; https://doi.org/10.3390/s22031050 - 28 Jan 2022
Cited by 9 | Viewed by 3946
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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29 pages, 2126 KiB  
Review
Development Technologies for the Monitoring of Six-Minute Walk Test: A Systematic Review
by Ivan Miguel Pires, Hanna Vitaliyivna Denysyuk, María Vanessa Villasana, Juliana Sá, Diogo Luís Marques, José Francisco Morgado, Carlos Albuquerque and Eftim Zdravevski
Sensors 2022, 22(2), 581; https://doi.org/10.3390/s22020581 - 12 Jan 2022
Cited by 16 | Viewed by 3437
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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18 pages, 1876 KiB  
Systematic Review
Smartphones and Threshold-Based Monitoring Methods Effectively Detect Falls Remotely: A Systematic Review
by Ricardo A. Torres-Guzman, Margaret R. Paulson, Francisco R. Avila, Karla Maita, John P. Garcia, Antonio J. Forte and Michael J. Maniaci
Sensors 2023, 23(3), 1323; https://doi.org/10.3390/s23031323 - 24 Jan 2023
Cited by 7 | Viewed by 2579
Abstract
In the US, at least one fall occurs in at least 28.7% of community-dwelling seniors 65 and older each year. Falls had medical costs of USD 51 billion in 2015 and are projected to reach USD 100 billion by 2030. This review aims [...] Read more.
In the US, at least one fall occurs in at least 28.7% of community-dwelling seniors 65 and older each year. Falls had medical costs of USD 51 billion in 2015 and are projected to reach USD 100 billion by 2030. This review aims to discuss the extent of smartphone (SP) usage in fall detection and prevention across a range of care settings. A computerized search was conducted on six electronic databases to investigate the use of remote sensing technology, wireless technology, and other related MeSH terms for detecting and preventing falls. After applying inclusion and exclusion criteria, 44 studies were included. Most of the studies targeted detecting falls, two focused on detecting and preventing falls, and one only looked at preventing falls. Accelerometers were employed in all the experiments for the detection and/or prevention of falls. The most frequent course of action following a fall event was an alarm to the guardian. Numerous studies investigated in this research used accelerometer data analysis, machine learning, and data from previous falls to devise a boundary and increase detection accuracy. SP was found to have potential as a fall detection system but is not widely implemented. Technology-based applications are being developed to protect at-risk individuals from falls, with the objective of providing more effective and efficient interventions than traditional means. Successful healthcare technology implementation requires cooperation between engineers, clinicians, and administrators. Full article
(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)
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24 pages, 1858 KiB  
Systematic Review
Machine Learning and Smart Devices for Diabetes Management: Systematic Review
by Mohammed Amine Makroum, Mehdi Adda, Abdenour Bouzouane and Hussein Ibrahim
Sensors 2022, 22(5), 1843; https://doi.org/10.3390/s22051843 - 25 Feb 2022
Cited by 43 | Viewed by 8879
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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