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Remote Healthcare with Sensors and Internet of Things
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Remote Healthcare with Sensors and Internet of Things

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

Deadline for manuscript submissions: 15 October 2024 | Viewed by 3105

Special Issue Editor

Prof. Dr. Liudi Jiang

E-Mail Website
Guest Editor
School of Engineering, University of Southampton, Southampton SO17 1BJ, UK
Interests: biomedical sensors; healthcare; IoT

Special Issue Information

Dear Colleagues,

There is an ongoing and growing interest in remote health monitoring, especially since the COVID-19 pandemic, which accelerated the drive toward the digital transformation of healthcare. For people with chronic health problems, monitoring their disease progression remotely is critical for clinicians to provide evidence-based and timely treatment. Sensor-based solutions, such as continuous blood glucose monitoring devices, smart inhalers and wearable biosensors, were some examples developed to assist data-driven treatment.

The rapid development of Internet of Things (IoT) solutions in recent years provides an immense opportunity to collect and disseminate health data in a central data platform for outcome measures and, in some cases, leads to new treatments. Therefore, novel sensing targeting medical applications combined with the fusion of IoT technologies have now become key for remote health monitoring.

This Special Issue will address topic areas that support remote health monitoring utilizing biomedical sensors via the IoT. This can include early-stage engineering, information communication and technology development, or later-stage applied patient trials.

We welcome scientific advancements in this field through original research articles, case series and literature reviews.

Prof. Dr. Liudi Jiang
Guest Editor

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 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.

Published Papers (3 papers)

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Research

20 pages, 1948 KiB  
Article
Liability of Health Professionals Using Sensors, Telemedicine and Artificial Intelligence for Remote Healthcare
by Marie Geny, Emmanuel Andres, Samy Talha and Bernard Geny
Sensors 2024, 24(11), 3491; https://doi.org/10.3390/s24113491 - 28 May 2024
Viewed by 217
Abstract
In the last few decades, there has been an ongoing transformation of our healthcare system with larger use of sensors for remote care and artificial intelligence (AI) tools. In particular, sensors improved by new algorithms with learning capabilities have proven their value for [...] Read more.
In the last few decades, there has been an ongoing transformation of our healthcare system with larger use of sensors for remote care and artificial intelligence (AI) tools. In particular, sensors improved by new algorithms with learning capabilities have proven their value for better patient care. Sensors and AI systems are no longer only non-autonomous devices such as the ones used in radiology or surgical robots; there are novel tools with a certain degree of autonomy aiming to largely modulate the medical decision. Thus, there will be situations in which the doctor is the one making the decision and has the final say and other cases in which the doctor might only apply the decision presented by the autonomous device. As those are two hugely different situations, they should not be treated the same way, and different liability rules should apply. Despite a real interest in the promise of sensors and AI in medicine, doctors and patients are reluctant to use it. One important reason is a lack clear definition of liability. Nobody wants to be at fault, or even prosecuted, because they followed the advice from an AI system, notably when it has not been perfectly adapted to a specific patient. Fears are present even with simple sensors and AI use, such as during telemedicine visits based on very useful, clinically pertinent sensors; with the risk of missing an important parameter; and, of course, when AI appears “intelligent”, potentially replacing the doctors’ judgment. This paper aims to provide an overview of the liability of the health professional in the context of the use of sensors and AI tools in remote healthcare, analyzing four regimes: the contract-based approach, the approach based on breach of duty to inform, the fault-based approach, and the approach related to the good itself. We will also discuss future challenges and opportunities in the promising domain of sensors and AI use in medicine. Full article
(This article belongs to the Special Issue Remote Healthcare with Sensors and Internet of Things)
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16 pages, 5068 KiB  
Article
Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment
by Ahmed S. Almasoud, Hanan Abdullah Mengash, Majdy M. Eltahir, Nabil Sharaf Almalki, Mrim M. Alnfiai and Ahmed S. Salama
Sensors 2023, 23(19), 8265; https://doi.org/10.3390/s23198265 - 6 Oct 2023
Cited by 1 | Viewed by 1050
Abstract
In recent years, the rapid progress of Internet of Things (IoT) solutions has offered an immense opportunity for the collection and dissemination of health records in a central data platform. Electrocardiogram (ECG), a fast, easy, and non-invasive method, is generally employed in the [...] Read more.
In recent years, the rapid progress of Internet of Things (IoT) solutions has offered an immense opportunity for the collection and dissemination of health records in a central data platform. Electrocardiogram (ECG), a fast, easy, and non-invasive method, is generally employed in the evaluation of heart conditions that lead to heart ailments and the identification of heart diseases. The deployment of IoT devices for arrhythmia classification offers many benefits such as remote patient care, continuous monitoring, and early recognition of abnormal heart rhythms. However, it is challenging to diagnose and manually classify arrhythmia as the manual diagnosis of ECG signals is a time-consuming process. Therefore, the current article presents the automated arrhythmia classification using the Farmland Fertility Algorithm with Hybrid Deep Learning (AAC-FFAHDL) approach in the IoT platform. The proposed AAC-FFAHDL system exploits the hyperparameter-tuned DL model for ECG signal analysis, thereby diagnosing arrhythmia. In order to accomplish this, the AAC-FFAHDL technique initially performs data pre-processing to scale the input signals into a uniform format. Further, the AAC-FFAHDL technique uses the HDL approach for detection and classification of arrhythmia. In order to improve the classification and detection performance of the HDL approach, the AAC-FFAHDL technique involves an FFA-based hyperparameter tuning process. The proposed AAC-FFAHDL approach was validated through simulation using the benchmark ECG database. The comparative experimental analysis outcomes confirmed that the AAC-FFAHDL system achieves promising performance compared with other models under different evaluation measures. Full article
(This article belongs to the Special Issue Remote Healthcare with Sensors and Internet of Things)
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11 pages, 1679 KiB  
Article
Optimization of Spatial and Temporal Configuration of a Pressure Sensing Array to Predict Posture and Mobility in Lying
by Silvia Caggiari, Liudi Jiang, Davide Filingeri and Peter Worsley
Sensors 2023, 23(15), 6872; https://doi.org/10.3390/s23156872 - 2 Aug 2023
Viewed by 973
Abstract
Commercial pressure monitoring systems have been developed to assess conditions at the interface between mattress/cushions of individuals at risk of developing pressure ulcers. Recently, they have been used as a surrogate for prolonged posture and mobility monitoring. However, these systems typically consist of [...] Read more.
Commercial pressure monitoring systems have been developed to assess conditions at the interface between mattress/cushions of individuals at risk of developing pressure ulcers. Recently, they have been used as a surrogate for prolonged posture and mobility monitoring. However, these systems typically consist of high-resolution sensing arrays, sampling data at more than 1 Hz. This inevitably results in large volumes of data, much of which may be redundant. Our study aimed at evaluating the optimal number of sensors and acquisition frequency that accurately predict posture and mobility during lying. A continuous pressure monitor (ForeSitePT, Xsensor, Calgary, Canada), with 5664 sensors sampling at 1 Hz, was used to assess the interface pressures of healthy volunteers who performed lying postures on two different mattresses (foam and air designs). These data were down sampled in the spatial and temporal domains. For each configuration, pressure parameters were estimated and the area under the Receiver Operating Characteristic curve (AUC) was used to determine their ability in discriminating postural change events. Convolutional Neural Network (CNN) was employed to predict static postures. There was a non-linear decline in AUC values for both spatial and temporal down sampling. Results showed a reduction of the AUC for acquisition frequencies lower than 0.3 Hz. For some parameters, e.g., pressure gradient, the lower the sensors number the higher the AUC. Posture prediction showed a similar accuracy of 63−71% and 84−87% when compared to the commercial configuration, on the foam and air mattress, respectively. This study revealed that accurate detection of posture and mobility events can be achieved with a relatively low number of sensors and sampling frequency. Full article
(This article belongs to the Special Issue Remote Healthcare with Sensors and Internet of Things)
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