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Remote healthcare is a well-accepted telemedicine service that renders efficient and reliable healthcare to patients suffering from chronic diseases, neurological disorders, diabetes, osteoporosis, sensory organs, and other ailments. Artificial intelligence, wireless communication, sensors, organic polymers, and wearables enable affordable, non-invasive healthcare to patients in all age groups. Telehealth services and telemedicine are beneficial to people residing in remote locations or patients with limited mobility, rehabilitation treatment, and post-operative recovery. Remote healthcare applications and services proved to be significant during the COVID-19 pandemic for both patients and doctors. This study presents a detailed study of the use of artificial intelligence and the internet of things in applications of remote healthcare in many domains of health, along with recent patents. This research also presents network diagrams of documents from the Scopus database using the tool VOSViewer. The paper highlights gap which can be undertaken by future researchers. © 2023 IEEE.
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The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. Note to Practitioners—In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate. IEEE
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Healthcare is an energy-intensive sector to protect the people who are infected by viruses such as COVID-19. Many countries during this tragic period have opened movable health care systems in the rural locality. The non-availability of the grid in rural areas creates a critical situation for the essential medical equipment to support patients during the widespread pandemic. Unfortunately most of these healthcare centers have been lacking the principles of sustainability and good health standards to become a go Green Health Care Center. A Green Health Care Center enhances patient well-being by utilizing natural resources in an efficient and environmental-friendly manner to all the people. The proposed Green Mobile Health Care Center (GMHCC) is a solar-powered system specially designed to supply medical loads effectively for 24 hours service. The system is designed in the form of an easily transmitted portable product to other places of mobile healthcare camps. © 2022 IEEE.
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The COVID-19 pandemic accelerated the use of remote patient monitoring in clinical practice or research for safety and emergency reasons, justifying the need for innovative digital health solutions to monitor key parameters or symptoms related to COVID-19 or Long COVID. The use of voice-based technologies, and in particular vocal biomarkers, is a promising approach, voice being a rich, easy-to-collect medium with numerous potential applications for health care, from diagnosis to monitoring. In this viewpoint, we provide an overview of the potential benefits and limitations of using voice to monitor COVID-19, Long COVID, and related symptoms. We then describe an optimal pipeline to bring a vocal biomarker candidate from research to clinical practice and discuss recommendations to achieve such a clinical implementation successfully.
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This study aims to investigate patients’ behavioral intention toward the adoption of contactless healthcare applications in the post- COVID-19 pandemic era. Therefore, the study model extends the unified theory of acceptance and use of technology (UTAUT) with the task technology fit (TTF) model, personal innovativeness, and avoidance of personal interaction to determine patients’ intention to adopt contactless healthcare applications for medical purposes. A research questionnaire was conducted on Jordanian citizens in a voluntary environment. In response, 383 valid questionnaires were retrieved. The study model is empirically analyzed with structural equation modeling (SEM). Findings of the structural model imply that was jointly predicted by UTAUT constructs, TTF, and API and explained substantial variance R2 78.4% in user behavior to adopt contactless healthcare applications. The current research contributes to theory by extending the UTAUT with the TTF model, API, and PI and enriching information systems literature in the context of users’ intention to adopt e-health technology. Practically, this research suggests that healthcare services providers should focus on IT fitness including internet-enabled devices and the number of facilities to operate the healthcare applications which in turn boost individual confidence towards the adoption of contactless healthcare technology. This research develops a unique model that examines user behavior towards the adoption of contactless healthcare technology to improve the healthcare industry. The findings of this research provide an answer on how to recover from COVID-19 repercussions on the healthcare sector while using such applications. Moreover, this study provides guidelines for clinical management through a virtual setting and guides health consultants, applications developers, and designers to design user-friendly applications for e-healthcare purposes. © 2022, IFIP International Federation for Information Processing.
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The pandemic crisis has obliterated human existence as we know it, as well as regional, social, and commercial action, as well as compelled human civilization in living inside the defined perimeter. Uses of IoT with ML in health care applications is described in this article. The created ML with IoT dependent observation prototype assists for tracing COVID-19 positive detected persons using prior information and isolates them from non-infected individuals. By anticipating as well as analyzing information with AI, proposed ML-IoT system employs parallel computing to track pandemic sickness and also to avoid pandemic disease. The use of machine learning-dependent IoT for COVID in health conditions diagnose likely to be demonstrated the effectiveness for detection and prevention of CORONAVIRUS transmission. It still effects in better way on lowering preventive expenditures also leds to better treatment for infected individuals. In terms of monitoring and tracking, the recommended technique is 95% accurate. The findings will aid for stopping the pandemic's spread and providing assistance to the healthcare sector. © 2022 IEEE.
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Analysing data on a large scale is becoming important and engages in convincing many researchers to use new platforms and tools that can handle large amounts of data. In this article, we present new evaluation sentiment analysis for large-scale datasets of COVID-19 Vaccine Stance tweets and COVID-19 Tweets IEEE data port datasets in the Apache Spark data system. The Apache Spark Scalable Machine Learning Library (ML) is used. We designed hybrid minhash models from the library with four classification methods: Logistic Regression (LR), Naive Bayes, Support Vector Machine and Random Forest classifiers in a parallel and distributed manner. In addition, Minhash with locality Sensitive hashing (Minhash-LSH) is compared to Minhash-ML. Performance parameters such as user, system and real time, time consumed, and accuracy have been applied in the comparative analysis to analyse the behaviour of the classifiers in the AWS spark Cluster, Local Spark cluster and in conventional system. Results have indicated that the models in spark environment was extremely effective for processing large-dimension data, which cannot be processed with conventional implementation or take much time related to some algorithms. The proposed model achieves accuracy above 99% in case of Vaccine tweet dataset when classified with Minhash- RF and Minhash- LR classifiers. Also, 100% in case of COVID-19 Tweets Provided by IEEE data port when using Minhash-SVM, Minhash-RF and Minhash-LR classifiers. © 2022 IEEE.
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The Coronavirus outbreak has become massive in recent years. World Health organization warned about the COVID-19 pandemic in March 2020. The United States' Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) kept tracking the pandemic effects and provided information on their websites. One such application is in healthcare, where COVID-19 patient health is tracked. The Internet of Things (IoT) increases medical equipment efficiency by allowing for practical tracking of health of affected patients, with wearable devices collecting information and reducing possible errors by humans. The collected details of a patient are transferred via a gateway from medical equipment to the Internet of Things, where they are stored and reviewed. One of the greatest roadblocks to the adoption of the could computing for medical applications is the tracking of each and every affected people from multiple places. Therefore, cloud computing in IoT offers a significant remedy for tracking people at minimum cost and improved disease treatment in the medical industry. The patient's body temperature and respiration are monitored. © 2022 IEEE.
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Most u-healthcare applications were developed based on five important dimensions: education, prevention, diagnosis, treatment, and monitoring of patients. The starting point of our research was the need of finding out the way in which the current COVID-19 pandemic influenced the dependency of chronic diseases’ persons over 50 to the M-health and E-health solutions in Romania. We focused on user’s experience and ability to use these systems, starting from the already known Smart-healthcare and U-healthcare ones, as developed in EU’s Horizon 2020 program and the way the new technologies like IoT, Smart Device, and Wearable Technologies as part of U-Healthcare are accepted and used in Romania. The focus group is made up of people aged 50 to 84, and the on-line questionnaire contained issues related to the acceptance of the IoT and the new technologies used in Telemedicine and U-healthcare systems for the prevention and monitoring of the chronic disease’s patients, starting from the influence UX factors. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Internet of Medical Things (IoMT) is an emerging technology whose capabilities to self-organize itself on-the-fly, to monitor the patient's vital health data without any manual entry and assist early human intervention gave birth to smart healthcare applications. The smart applications can be used to remotely monitor isolated patients during this COVID-19 pandemic. Remote patient monitoring provides an opportunity for COVID-19 patients to have vital signs and other indicators recorded regularly and inexpensively to provide rapid and early warning of conditions that require medical attention using secured edge and cloud computing. However, to gain the confidence of the users over these applications, the performance of healthcare applications should be evaluated in real-time. Our real-time implementation of IoMT based remote monitoring application using edge and cloud computing, along with empirical evaluation, show that COVID-19 patients can be monitored effectively not only with mobility but also helps the health care professionals to generate consolidated health data of the patient that can guide them to obtain medical attention. © 2021 IEEE.
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Modern healthcare uses wireless technology for automatic remote monitoring of the patients. The IoT plays a significant role in a broad range of healthcare applications, such as managing chronic diseases or disease prevention. This paper presents the design and implementation of the IoT platform to monitor vital signs, mainly oxygenation of the blood, body temperature and pulse rate. The aim of the project was to support students to improve their technical skills during distance learning. The main goal of this project was to design a platform that allowed a patient with Covid disease to measure blood oxygenation at home. The outcome of the project is a wearable IoT solution, of which the HW part is based on the Arduino UNO controller, Bluetooth communication module and a low-power pulse oximeter sensor. Besides measuring oxygenation of the blood, it can monitor other vital signs such as heart pulse and body temperature. © 2021 IEEE.
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The Internet of Medical Things (IoMT) is a set of medical devices and applications that connect to healthcare systems through the Internet. Those devices are equipped with communication technologies that allow them to communicate with each other and the Internet. Reliance on the IoMT is increasing with the increase in epidemics and chronic diseases such as COVID-19 and diabetes;with the increase in the number of IoMT users and the need for electronic data sharing and virtual services, cyberattacks in the healthcare sector for accessing confidential patient data has been increasing in the recent years. The healthcare applications and their infrastructures have special requirements for handling sensitive users' data and the need for high availability. Therefore, securing healthcare applications and data has attracted special attention from both industry and researchers. In this paper, we propose a Federated Transfer Learning-based Intrusion Detection System (IDS) to secure the patient's healthcare-connected devices. The model uses Deep Neural Network (DNN) algorithm for training the network and transferring the knowledge from the connected edge models to build an aggregated global model and customizing it for each one of the connected edge devices without exposing data privacy. CICIDS2017 dataset has been used to evaluate the performance in terms of accuracy, detection rate, and average training time. In addition to preserving data privacy of edge devices and achieving better performance, our comparison indicates that the proposed model can be generalized better and learns incrementally compared to other baseline ML/DL algorithms used in the traditional centralized learning schemes. © 2021 IEEE.
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Medical data is becoming more dense and complicated day by data. After COVID-19, the medical information is entirely expended from terabytes and petabytes. An accurate diagnosis needs a sophisticated mechanism and the support of information technology. Hadoop ecosystem is facilitating big data management for various health care applications. As dense patient history leads to better diagnosis;Hadoop architecture supports patient data accommodation, retrieval, update, and many similar functions like information assortment, information intricacy, information stockpiling, information investigation, information security, and protection. © 2021 IEEE.
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Amidst the current covid-19 pandemic situation continuous health monitoring becomes important. In this work, we propose a low cost portable healthcare module which helps in tracking a patient's health conditions using various parameters such as heart rate, carbon dioxide exhalation, body temperature and electrical heart recording (ECG). This monitoring can be done autonomously without the presence of a doctor. This module is helpful in the health monitoring of patients who are in quarantine, or under treatment in a hospital. It can also be used for the health monitoring of elderly and diabled patients. In this work, we also compare some of the existing modules and draw a comparison. In addition to that we also compare different machine learning algorithms used for prediction of asthma. Our results for different algorithms have been quantified and we found that using K neighbors we got the maximum score of 87%. © 2021 IEEE.
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Photoplethysmography (PPG) sensing is a popular optical method used to determine blood oxygen saturation and heart rate based on light reflected from a patient's skin. Both health metrics are useful in detecting COVID-19 in asymptomatic patients but remain impacted by physiological differences in individuals. In the context of wearable self-powered devices, PPG sensing is relatively high-power compared to available on-body energy. This paper presents a PPG sensing model, with a transimpedance amplifier (TIA) to demonstrate power, signaling, and design tradeoffs, and a photodiode model that includes the impact of a patient's skin phototype on reflected light and PPG sensing accuracy. It also presents preliminary measured results from on-body testing with existing hardware to verify the power and ability to extract a PPG signal at those power levels. This model demonstrates the need to first identify the minimum allowable photodiode current that can produce accurate results for each skin phototype and then determine user-specific circuit knobs to achieve personalized PPG sensing with optimized power consumption. © 2021 IEEE.
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The Hajj pilgrimage being the largest annual mass gathering globally with two to three million participants from over 180 counties, will remain a high priority for diseases surveillance for future epidemics or any other international public health emergencies with rapid scalability. This paper highlights the importance of monitoring mass gatherings during a pandemic and how mHealth applications can reduce the burden on health facilities during a mass gathering and tackle future infectious diseases outbreaks. The paper also highlights the importance of developing a user-centred application when designing for a diverse group of users with a shared purpose. As a result, a framework has been proposed to update the current applications or design and develop future mobile health applications. The framework has been developed based on the rationale and evidence found in the literature. © Islam et al. Published by BCS Learning and Development Ltd.
ABSTRACT
IOT technology is proved to be one of the key players in attracting the attention towards itself in the recent years, for the potential of it to be the part of our healthcare Systems. Among the applications that Internet of Things (IoT) encouraged to the world, Healthcare applications are generally significant. By and large, IoT has been broadly used to interconnect the high-level clinical assets and to offer brilliant and compelling medical care administrations to individuals. The high-level sensors can be either worn or be implanted into the body of the patients, in order to constantly screen their wellbeing. The proposed system is Health Measure Kit and Finding a Potential Covid-19 Suspect using IOT. The proposed system will determine the health of the person in an all in one portable solution using IOT and android application. This work will not be limited to only a portable Health Measure kit/System but our device can also find a Potential Covid-19 Suspect. The work will consist of SPO2 sensor to determine amount of blood oxygen level in the blood of the user, the heart rate sensor to determine the beats per minute, temperature sensor which can tell the temperature of the user just by positioning the index finger on the sensor and an unique lung capacity checker. Based on the above statistical data, and health score calculation using our system, one can identify a potential suspect case of Covid-19 and send his/her information to the health department of India to decrease and stop spread of virus. Improve the efficiency of checking at entry points for various places. Creating awareness on health issues. Helping government in creating an online health ID for every individual which will/can be linked with their government IDs. © 2021 IEEE.