The prevalence of COVID-19 has changed traditional teaching modes. For many teachers, online learning effectively compensated for the absence of traditional face-to-face instruction. Online learning can support students and schools and can create unique opportunities under emergency management. Educational institutions in various countries have launched large-scale online course modes in response to the pandemic. Additionally, online learning during a pandemic differs from traditional online learning modes. Through surveying students in higher education institutions, educational reform under emergency management can be explored. Therefore, university students were surveyed to investigate their continuance intention regarding online learning during the pandemic. Expectation confirmation theory was extended using the task-technology fit model to ascertain whether the technical support of promoting online learning helped student’s complete course learning tasks during the pandemic and spawned a continuance intention to use online learning in the future. Data were collected through online questionnaires. A total of 854 valid responses were collected, and partial least squares structural equation modeling was employed to verify the research hypotheses. The results revealed that the overall research framework largely explained continuance intention. Concrete suggestions are proposed for higher education institutions to promote online learning modes and methods after the COVID-19 pandemic.

https://www.mdpi.com/2071-1050/13/8/4586/pdf

Continuance Intention of University Students and Online Learning during the COVID-19 Pandemic: A Modified Expectation Confirmation Model Perspective

Many educational institutions have adopted e-learning under COVID-19 pandemic to maintain school teaching activities. Most teachers were encouraged to use online instruction in early February 2020. Thus, whether online learning changes students’ learning habits and replaces traditional physical teaching methods, online learning has become a keen topic. Based on the push–pull–mooring model, we proposed a comprehensive research model and explored the impact of online learning during COVID-19 pandemic on students’ attitude and behavioral intention. We found that push effects (perceived security risk, learning convenience, and service quality), pull effects (usefulness, ease of use, teacher’s teaching attitude, task-technology fit), and mooring effects (switching cost, habit) had significantly influence the switching intentions of users from physical course to online learning platforms. The findings of this study will bring more insights into e-learning during an epidemic crisis.

/pdf/factors-influence-students-switching-behavior-to-online-jismkary37.pdf

Factors Influence Students’ Switching Behavior to Online Learning under COVID-19 Pandemic: A Push–Pull–Mooring Model Perspective

Internet of Medical Thing (IoMT) is the most emerging era of the Internet of Thing (IoT), which is exponentially gaining researchers' attention with every passing day because of its wide applicability in Smart Healthcare systems (SHS). Because of the current pandemic situation, it is highly risky for an individual to visit the doctor for every small problem. Hence, using IoMT devices, we can easily monitor our day-to-day health records, and thereby initial precautions can be taken on our own. IoMT is playing a crucial role within the healthcare industry to increase the accuracy, reliability, and productivity of electronic devices. This research work provides an overview of IoMT with emphasis on various enabling techniques used in smart healthcare systems (SHS), such as radio frequency identification (RFID), artificial intelligence (AI), and blockchain. We are providing a comparative analysis of various IoMT architectures proposed by several researchers. Also, we have defined various health domains of IoMT, including the analysis of different sensors with their application environment, merits, and demerits. In addition, we have figured out key protocol design challenges, which are to be considered during the implementation of an IoMT network-based smart healthcare system. Considering these challenges, we prepared a comparative study for different data collection techniques that can be used to maintain the accuracy of collected data. In addition, this research work also provides a comprehensive study for maintaining the energy efficiency of an AI-based IoMT framework based on various parameters, such as the amount of energy consumed, packet delivery ratio, battery lifetime, quality of service, power drain, network throughput, delay, and transmission rate. Finally, we have provided different correlation equations for finding the accuracy and efficiency within the IoMT-based healthcare system using artificial intelligence. We have compared different data collection algorithms graphically based on their accuracy and error rate. Similarly, different energy efficiency algorithms are also graphically compared based on their energy consumption and packet loss percentage. We have analyzed our references used in this study, which are graphically represented based on their distribution of publication year and publication avenue.

/pdf/internet-of-medical-things-iomt-based-smart-healthcare-2bpkpqro.pdf

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

The proliferation in embedded and communication technologies made the concept of the Internet of Medical Things (IoMT) a reality. Individuals’ physical and physiological status can be constantly monitored, and numerous data can be collected through wearable and mobile devices. However, the silo of individual data brings limitations to existing machine learning approaches to correctly identify a user’s health status. Distributed machine learning paradigms, such as federated learning, offer a potential solution for privacy-preserving knowledge sharing without sending raw personal data. However, federated learning is vulnerable to harmful participants that can degrade the overall model quality by sharing low-quality data. Therefore, it is critical to select suitable participants to ensure the accuracy and efficiency of federated learning. In this article, a unique clustering-based approach is proposed to use social context data for participant selection. Different edge participant groups will be established, and group-specific federated learning will be performed. The models of various edge groups will be further aggregated to strengthen the robustness of the global model. The experimental results demonstrated that through participant selection, clustering-based hierarchical federated learning can achieve better results with less participants in two different IoMT applications for ECG and human motion monitoring. This shows the efficacy of the proposed method in improving federated learning performance and efficiency in various IoMT applications. 

Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications

The Internet of Medical Things (IoMT) has created a wide range of opportunities for knowledge exchange in numerous industries. The opportunities include patient empowerment, healthcare collaboration, medical education and training, remote monitoring and telemedicine, customized treatment plans, data sharing for innovation, continuous medical learning, supply chain management, public health initiatives, wearable health devices, and quality improvement initiatives. However, the adoption of IoMT faces numerous challenges regarding interoperability, data privacy, security, regulatory, and infrastructure costs. This paper aims to address the implications of data fusion in IoMT, as well as the associated security challenges and their potential solutions, which are lacking in the literature. Data collected from IoMT devices has a direct impact on the accuracy of predictions because of its quality, quantity, and relevance. With an accuracy of 99.53% to 99.99%, the Epilepsy seizure detector-based Naive Bayes (ESDNB) algorithm is found to be the most effective for detecting epileptic seizures in IoMT networks. However, the way data are stored must also undergo a major revolution, and all phases—collection, protection, and storage—need to be improved. The standardization of architecture and security measures may improve the detection of security threats and compromises. Methods to detect malware in cross platforms is also an avenue for future research that can effectively tackle the heterogeneity of the IoMT systems. Cryptography and blockchain technology have shown to be promising ways to increase the security of an IoMT-based system. The findings of this review will assist a wide variety of stakeholders in the healthcare ecosystem. 

Insights into Internet of Medical Things (IoMT): Data fusion, security issues and potential solutions

The heart is one of the most important organs of the human body It circulates blood throughout the human body and delivers oxygen and nutrients to all the organs for metabolism Cardiac muscle contraction results in blood circulation, which maintains the body temperature at approximately 37 °C If the cardiac function is abnormal, then the body temperature will be affected The cardiac function degenerates as the human body ages, and the degeneration can occasionally result in cardiovascular diseases When the Internet of Medical Things is integrated into heart disease screening systems to detect heart diseases, people can perform self-examinations to evaluate whether their hearts exhibit irregularities for early heart disease detection STM32 is used in this study as the main Internet-of-Medical Things controller and is combined with the Internet of Things devices—a sphygmomanometer cuff, temperature sensor, and pulse sensor—for instrument control and data acquisition This assembly is used to develop a valvular heart disease screening system, whose structure incorporates deep learning for the development of fitting models and analysis An experiment is performed where blood flow is blocked temporarily and released to observe changes in the surface temperature of the fingertip skin, and the blood supply capability of the heart is assessed indirectly based on the temperature change curve Eighteen subjects were recruited in the experiment, where one subject exhibited cardiac valve insufficiency and arrhythmia In the experiment, temperature curve variation data are successfully obtained from the healthy subjects, whereas the temperature curve irregularities of the patient with cardiac valve insufficiency are identified This subject’s temperature range throughout three test steps is smaller by within 052 °C compared with those of most of the other subjects In addition, during blood blocking and release, the overall temperature curve decreases, whereas some curves escalate first before plummeting slowly The data analysis results show that the temperature curve variation and values of Subject 2 are similar to those of Subject 10, suggesting the incidence of valvular heart disease This valvular heart disease screening system can successfully analyze and assess the characteristic signal values of patients with valvular heart disease

Deep Learning Methods in Internet of Medical Things for Valvular Heart Disease Screening System

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Deep Learning Methods in Internet of Medical Things for Valvular Heart Disease Screening System