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During the COVID-19 pandemic, the world has witnessed rampant use of digital technology in almost every facet of huma lives. However, these digital techniques like use of mobile apps have certain drawbacks and constrains. These could be addressed with the use of emerging technologies like Internet of Things (IoT). In healthcare, that is Internet of Medical Things (IoMT). A number of ongoing studies show that integrating security measures with technology will lead to the adoption of stable IoMT applications. Emerging IoMT technologies used in conjunction with Blockchain, Artificial Intelligence, and Big Data, provide feasible solutions. The paper focuses on IoMT models, innovations, and security progress made to tackle COVID-19 issues. This paper contains the bibliometric research highlighting the use of disruptive technology for the diagnosis of COVID-19. It reviews the adoption of contactless services in the post-COVID-19 period along with potential opportunities as well as challenging obstacles for healthcare agencies, policymakers, and customers. [ABSTRACT FROM AUTHOR]

The world has experienced a digital transformation of epic proportions in the past few years. The prospects of the Internet of Things for improving efficiency, reducing cost, achieving data-oriented predictive maintenance, and enhancing quality have now been proven well by various studies. Since everything is going online, a lot of data is available at the disposal of organizations. Useful insights can be gleaned from these large data sets, which comprise real-time data from various devices with intelligent sensors, and seamless integration. Here is precisely where Smart Devices come into the picture. IoT and smart devices are critical enablers for higher customer satisfaction, energy efficiency, and personalization. IoT-enabled smart devices like intelligent wearables (fit-bit, iWatch), voice assistants (Google Home, Alexa), smart refrigerators, smart washing machines, etc., are becoming widespread, commercially available. People using these develop routines and become dependent on them. This entire ecosystem consisting of IoT, smart devices, technologies, and the internet is highly consumer-centric and eventually leads to satisfied and happy customers. Quantitative research involving the collection and analysis of data will be carried out. Primary data will be collected by floating a questionnaire. Data from Indians (age group 18-60) will be collected using a questionnaire. Using SPSS, the descriptive analysis would be carried out on the data collected to find the mean, standard deviation, correlation, etc. of the data. Technology Acceptance Model (TAM) is incorporated in the research to comprehend how receptive or averse are people when it comes to IoT-enabled smart devices. The model used comprises of a factor, namely Social Context Factor which includes the Social Influence construct, and technology factors which include the following constructs, Trust, Perceived Usefulness (PU), and Perceived Ease of Use (PEOU). TAM will help one understand a user's Behavioural Intention to use IoT-enabled smart devices. The paper would help one derive the impact of technical factors and social context factors on the acceptance of IoT-enabled smart devices. The paper tries to explore the various driving factors when it comes to an individual's willingness to use IoT-enabled smart devices from different prerogatives i.e. technology, and social context. The research paper successfully extends TAM to such acceptance. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the world has witnessed rampant use of digital technology in almost every facet of huma lives. However, these digital techniques like use of mobile apps have certain drawbacks and constrains. These could be addressed with the use of emerging technologies like Internet of Things (IoT). In healthcare, that is Internet of Medical Things (IoMT). A number of ongoing studies show that integrating security measures with technology will lead to the adoption of stable IoMT applications. Emerging IoMT technologies used in conjunction with Blockchain, Artificial Intelligence, and Big Data, provide feasible solutions. The paper focuses on IoMT models, innovations, and security progress made to tackle COVID-19 issues. This paper contains the bibliometric research highlighting the use of disruptive technology for the diagnosis of COVID-19. It reviews the adoption of contactless services in the post-COVID-19 period along with potential opportunities as well as challenging obstacles for healthcare agencies, policymakers, and customers. [ABSTRACT FROM AUTHOR]

The world has experienced a digital transformation of epic proportions in the past few years. The prospects of the Internet of Things for improving efficiency, reducing cost, achieving data-oriented predictive maintenance, and enhancing quality have now been proven well by various studies. Since everything is going online, a lot of data is available at the disposal of organizations. Useful insights can be gleaned from these large data sets, which comprise real-time data from various devices with intelligent sensors, and seamless integration. Here is precisely where Smart Devices come into the picture. IoT and smart devices are critical enablers for higher customer satisfaction, energy efficiency, and personalization. IoT-enabled smart devices like intelligent wearables (fit-bit, iWatch), voice assistants (Google Home, Alexa), smart refrigerators, smart washing machines, etc., are becoming widespread, commercially available. People using these develop routines and become dependent on them. This entire ecosystem consisting of IoT, smart devices, technologies, and the internet is highly consumer-centric and eventually leads to satisfied and happy customers. Quantitative research involving the collection and analysis of data will be carried out. Primary data will be collected by floating a questionnaire. Data from Indians (age group 18-60) will be collected using a questionnaire. Using SPSS, the descriptive analysis would be carried out on the data collected to find the mean, standard deviation, correlation, etc. of the data. Technology Acceptance Model (TAM) is incorporated in the research to comprehend how receptive or averse are people when it comes to IoT-enabled smart devices. The model used comprises of a factor, namely Social Context Factor which includes the Social Influence construct, and technology factors which include the following constructs, Trust, Perceived Usefulness (PU), and Perceived Ease of Use (PEOU). TAM will help one understand a user's Behavioural Intention to use IoT-enabled smart devices. The paper would help one derive the impact of technical factors and social context factors on the acceptance of IoT-enabled smart devices. The paper tries to explore the various driving factors when it comes to an individual's willingness to use IoT-enabled smart devices from different prerogatives i.e. technology, and social context. The research paper successfully extends TAM to such acceptance. [ABSTRACT FROM AUTHOR]