IoT and its Significance on Smart Technologies in the Health Sector-An Extensive Review

IoT seems to be the trending solution in all sectors notably because of the yield in productivity, efficiency, effective strategies, and results that are associated with adapting to this technology. These positive results are enormously experienced in one of the crucial sectors which determine and ensure the prolonged healthy life expectancy of mankind. It is well noted that a lot of work has been done on this topic in Academia and Corporate field all over the world but this paper will present a selective review that has been done so far by the Academic world as a scholarly article and a resource for the Health Sector in Fiji to earnestly integrate smart technologies in its architecture. © 2022 IEEE.

The promise and challenges of the fourth industrial revolution (4IR)

The fourth Industrial Revolution is upon us. Yet not many students understand its evolution or impacts. This teaching case looks at socio-technical evolution from 1IR (First Industrial Revolution) to 4IR (Fourth Industrial Revolution). The teaching case concludes by exemplifying 4IR's promises and perils with a mini case on how 4IR that quietly helped the COVID-19 vaccine development can also pose cyberthreats and erode privacy. AD -, Kent, OH, USA;, Johannesburg, South Africa ;, Kent, OH, USA;, Johannesburg, South Africa

Aimed Robot: Autonomous & IoT Controlled Medical Assistance Robot

This paper describes the emerging role of robots in health care and integrated environments with special concerns related to the management and control of the spread of novel coronavirus 2019. The primary use of such robots is to reduce human interaction and ensure cleanliness, fertility, and support in the hospitals and facilities such as isolation. This will lead to a reduction in the life-threatening risk for medical professionals and physicians who have played a significant role in the management of infectious diseases like coronavirus. The purpose of this work is to highlight the importance of medical robots in general and to link their use in the field of medical assistance so that hospital administrators can use the medical robots for different treatment processes. The work involves the design and development of an AUTONOMOUS and IoT CONTROLLED MEDICAL ASSISTANCE ROBOT (AIMED), which is designed as a potential answer for any irresistible ailment, particularly for Coronaviruses Outbreak. AIMED ROBOT is utilized to limit individual to-individual contact and to guarantee cleaning, sanitization and backing in emergency clinics and comparable offices, for example, food and medication conveyance in isolate emergency clinics/offices. Voice communication can also be established between the control room and patient through a wireless network. This data is sent to the doctor and staff using a communication network that helps in monitoring the patient's condition without human interaction. Hand Sanitization, food delivery and waste collection system are also provided in the robot for quarantine zones. This is despite the popularity of telemedicine, which also applies to similar situations. In fact, the recent success of the Korean and Chinese health sectors in gaining effective control of the coronavirus epidemic would not have been possible without the use of state-of-the-art technology. Background(s): In the quick advancement of innovation, there are numerous sorts of robots with different details and capacities. The AIMED Robots are being developed for hospitals to deliver medicines, food and for taking live data from the patient directly. A few issues are confronted in regards to the limitation of explicit places around and inside the clinic because different robots were utilizing landmark recognition. Moreover, the issues faced when the robot is to convey things to any patient from any place should be taken care of. Objective(s): The objective of this work is to highlight the importance of medical robots in general and to link their use in the field of medical assistance so that hospital administrators can use the medical robots for different treatment processes. Method(s): The work involves the design and development of an AUTONOMOUS and IoT CONTROLLED MEDICAL ASSISTANCE ROBOT (AIMED), which is designed as a potential answer for any irresistible ailment particularly for Coronaviruses Outbreak. Result(s): AIMED Robot goes through three testing stages. In the final stage, one complete embedded system is designed by embedding each tested sub-system and then this final embedded system is tested. All the project goals, which were planned as 'Solution to Problem Statement', have been achieved properly. Conclusion(s): This paper provides an overview of the robotics and therapeutic potential of robotics in specific environments with COVID-19 epidemic control. The AIMED robot introduced in this paper is a piece of emergency clinic and care focus computerization framework. Multiple patients are placed for stop-over and doctor's prescription regarding patient health. It can be very useful to counter infectious diseases like SARS, MERS or Covid-19 etc. This work confirms that the introduction of medical robots has significantly increased the safety and quality of health care systems compared to manual systems due to the digitalization of health care.Copyright © 2021 Bentham Science Publishers.

Batteryless wireless magnetostrictive Fe30Co70/Ni clad plate for human coronavirus 229E detection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been garnered increasing for its rapid worldwide spread. Each country had implemented city-wide lockdowns and immigration regulations to prevent the spread of the infection, resulting in severe economic consequences. Materials and technologies that monitor environmental conditions and wirelessly communicate such information to people are thus gaining considerable attention as a countermeasure. This study investigated the dynamic characteristics of batteryless magnetostrictive alloys for energy harvesting to detect human coronavirus 229E (HCoV-229E). Light and thin magnetostrictive Fe-Co/Ni clad plate with rectification, direct current (DC) voltage storage capacitor, and wireless information transmission circuits were developed for this purpose. The power consumption was reduced by improving the energy storage circuit, and the magnetostrictive clad plate under bending vibration stored a DC voltage of 1.9 V and wirelessly transmitted a signal to a personal computer once every 5 min and 10 s under bias magnetic fields of 0 and 10 mT, respectively. Then, on the clad plate surface, a novel CD13 biorecognition layer was immobilized using a self-assembled monolayer of -COOH groups, thus forming an amide bond with -NH2 groups for the detection of HCoV-229E. A bending vibration test demonstrated the resonance frequency changes because of HCoV-229E binding. The fluorescence signal demonstrated that HCoV-229E could be successfully detected. Thus, because HCoV-229E changed the dynamic characteristics of this plate, the CD13-modified magnetostrictive clad plate could detect HCoV-229E from the interval of wireless communication time. Therefore, a monitoring system that transmits/detects the presence of human coronavirus without batteries will be realized soon.

A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks.

The unexpected emergence of the COVID-19 pandemic has changed how grocery shopping is done. The grocery retail stores need to ensure hygiene, quality, and safety concerns in-store shopping by providing "no-touch" smart packaging solutions for agri-food products. The benefit of smart packaging is to inform consumers about the freshness level of a packaged product without having direct contact. This paper proposes a data-driven decision support system that uses smart packaging as a smart product-service system to manage the sustainable grocery store supply chain during outbreaks to prevent food waste. The proposed model dynamically updates the price of a packaged perishable product depending on freshness level while reducing food waste and the number of rejected customers and maximising profit by increasing the inventory turnover rate of grocery stores. The model was tested on a hypothetical but realistic case study of a single product. The results of this study showed that stock capacities, freshness discount rate, freshness period, and quantity discounts significantly affect the performance of a grocery store supply chain during outbreaks.

IoT based Smart Mask for Health Monitoring

COVID-19 virus is a source of concern and hazard in modern times. With a huge population moving about and an insufficient task force and resources to administer them, manual monitoring of social distance standards is impossible. As a result, this research work attempts to develop a smart electronic mask to maintain social distance while monitoring other health markers. Healthcare has received a lot of research attention in recent years. Several human instances of new coronavirus infection were verified during the end of 2019 and the beginning of 2020. As a result of the outbreak of covid-19, wearing a face mask has become required in several nations, and its effectiveness in preventing the pandemic has been demonstrated. During interactions, wearing a mask accidentally draws individuals closer together. Even if you wear a mask, it will not be enough to protect you from the corona virus. Even when wearing a mask, social distance should be maintained. The developed smart mask includes different functions such as social distancing, health monitoring, and mask hygiene monitoring. If someone comes too close, the distance sensor linked to the mask will alert the individuals. This will be linked to a little buzzer for alerting purposes. Temperature calculation is critical for COVID-19 detection, and many nations still employ it as a quick test to determine whether visitors or persons are infected. In the case of a pandemic, our project has designed a smart and hygienic mask that may be utilized. The corona virus is protected with this reusable and sanitary Covid- 19 mask. The health monitoring and warning system will be activated while you are wearing the mask. Bluetooth will be used to see the biosensor information. To keep the mask clean, sensors on the mask will detect any wetness or strange odour. © 2022 IEEE.

Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model.

This paper proposes a time-series stochastic socioeconomic model for analyzing the impact of the pandemic on the regulated distribution electricity market. The proposed methodology combines the optimized tariff model (socioeconomic market model) and the random walk concept (risk assessment technique) to ensure robustness/accuracy. The model enables both a past and future analysis of the impact of the pandemic, which is essential to prepare regulatory agencies beforehand and allow enough time for the development of efficient public policies. By applying it to six Brazilian concession areas, results demonstrate that consumers have been/will be heavily affected in general, mainly due to the high electricity tariffs that took place with the pandemic, overcoming the natural trend of the market. In contrast, the model demonstrates that the pandemic did not/will not significantly harm power distribution companies in general, mainly due to the loan granted by the regulator agency, named COVID-account. Socioeconomic welfare losses averaging 500 (MR$/month) are estimated for the equivalent concession area, i.e., the sum of the six analyzed concession areas. Furthermore, this paper proposes a stochastic optimization problem to mitigate the impact of the pandemic on the electricity market over time, considering the interests of consumers, power distribution companies, and the government. Results demonstrate that it is successful as the tariffs provided by the algorithm compensate for the reduction in demand while increasing the socioeconomic welfare of the market.

IoHT-based mass surveillance in smart cities to prevent the spread of pandemics

Healthcare IoT (Internet of Things) is an emerging technology that eases human life by connecting smart devices and feeding us valuable data. It plays a valuable role in healthcare applications (IoHT- Internet of Health Things) by providing precise monitoring, real-time data feedback, and remote diagnosis. IoHT (Internet of Health Things), when connected to a wide-area connection system like a smart city, will be an advancement in the healthcare and lifestyle of people in this catastrophic period of the spread of the COVID-19 virus. The data gathered by IoHT devices is uploaded to the control and healthcare server, where the data is analyzed to prevent the communication of disease and to improvise the city's functioning, hence making the city a smart city to deal with this situation efficiently. In this paper, we will try to work on the IoHT-based system for surveillance of smart cities during pandemics. © 2022 Nova Science Publishers, Inc.

Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning.

Vaccination offers health, economic, and social benefits. However, three major issues-vaccine quality, demand forecasting, and trust among stakeholders-persist in the vaccine supply chain (VSC), leading to inefficiencies. The COVID-19 pandemic has exacerbated weaknesses in the VSC, while presenting opportunities to apply digital technologies to manage it. For the first time, this study establishes an intelligent VSC management system that provides decision support for VSC management during the COVID-19 pandemic. The system combines blockchain, internet of things (IoT), and machine learning that effectively address the three issues in the VSC. The transparency of blockchain ensures trust among stakeholders. The real-time monitoring of vaccine status by the IoT ensures vaccine quality. Machine learning predicts vaccine demand and conducts sentiment analysis on vaccine reviews to help companies improve vaccine quality. The present study also reveals the implications for the management of supply chains, businesses, and government.

IoT Deployable Lightweight Deep Learning Application For COVID-19 Detection With Lung Diseases Using RaspberryPi

Proper assessment of COVID-19 patients has become critical to mitigating and halting the disease's rapid expansion during the present COVID-19 epidemic across the nations. Due to the presence of chronic lung/pulmonary diseases, the intensity and demise rates of COVID-19 patients were increased. This study will analyze radiography utilizing chest X-ray images (CXI), one of the most successful testing methods for COVID-19 case identification. Given that deep learning (DL) is a useful method and technique for image processing, there have been several research on COVID-19 case identification using CXI to train DL models. While few of the study claims outstanding predictive outcomes, their suggested models may struggle with overfitting, excessive variance, and generalization mistakes due to noise, a limited number of datasets and could not be deployed to IoT devices due to heavy network size. Considering deep Convolutional Neural Network (CNN) can conquer the weaknesses by getting predictions with several diseases using a single model deployed on a real-time IoT device. We propose a lightweight Deep Learning model (LDC-Net) that has spearheaded an open-sourced COVID-19 case identification technique using CNN-generated CXI by utilizing a suggested strategy aware of distinct features learning of different classes. Experimental results on Raspberry Pi show that LDC-Net provides encouraging outputs for detecting COVID-19 cases with an overall 96.86% precision, 96.78% recall, 96.77% F1-score, and 99.28% accuracy, better than other state-of-the-art models. By empowering the Internet of Things-IoT and IoMT devices, this suggested framework can identify COVID-19 from CXI and other seven lung diseases with healthy labels. © 2022 IEEE.

New-normal Market Entry Mode for Pharmaceuticals: an Internet of Things (IoT) market entry framework stemming from COVID-19

Purpose: This paper aims to determine new-normal uncertainty considerations stemming from the COVID-19 pandemic to consider within transaction-cost analysis for pharmaceuticals. It also aims to propose new-normal market entry strategies to address the uncertainty as a result of COVID-19's implications and provide for lack of knowledge and information in an uncertain business environment by way of Internet of Things (IoT) ecosystem for pharmaceutical market entry. Design/methodology/approach: In this paper, we focus on the uncertainty facet within transaction-cost analysis consideration and utilise a descriptive three-case study approach taking in Johnson and Johnson (J&J), GlaxoSmithKline (GSK) and Novartis to present an ADO (Antecedent-Decisions-Outcomes) understanding of their usual market entry approach, the approach undertaken during the pandemic and the outcomes thereafter facilitating new-normal uncertainty considerations to factor in. Further with this insight, we develop a conceptual framework addressing the transaction-cost analysis implications of uncertainties toward lack of knowledge and information for a new-normal market entry approach and operating strategy for pharmaceuticals applicable due to IoT (Internet of Things). Findings: Uncertainty (external and internal) is different now in the new-normal business environment for pharmaceuticals and boils down to acute shortage of knowledge and information impact to make an appropriately informed decision. Therefore, considering the changed factors to consider, pharmaceuticals need to be able to undertake market entry with vaccines and medicines by way of IoT thereby enabling, the filling of the gap via real-time data access and sharing, including enhancing predictive analysis for sustenance. Research limitations/implications: The paper's findings have many theoretical implications highlighted in the manuscript. Practical implications: The paper's findings have many practical implications highlighted in the manuscript. Originality/value: This is the first study to our knowledge that throws light on transaction-cost analysis theory's uncertainty facet for pharmaceuticals. It is also the first study that provides a new-normal market entry strategy for pharmaceutical companies built on interoperability of real-time IoT. © 2022, Emerald Publishing Limited.

IOT-Based Intelligent Assistant Mirror For Smart Life & Daily Routine Using Raspberry PI

Humans start their day by looking in the mirror at least once before leaving their homes every morning. In addition, they waste some considerable time of their busy workload in front of the mirror. To make this time more productive and useful, there ought to be a system that can be readily conducted, user-friendly, and smart according to the constant progress on the Internet of Things. The intelligent mirror is a new addition to the smart device family, which is a straightforward concept. There will be a screen placed behind a two-way mirror, and this Intelligent Mirror turns our room or bathroom mirror into a personal assistant with artificial intelligence. The purpose is to develop a smart mirror that can automate working humans' busy daily routines and manage their tasks when they spend their time in front of a mirror. To make the most of this moment, users can securely access all the relevant details of the day by looking in the mirror simultaneously. The intelligent mirror, which a single voice command can activate, will significantly help disabled persons and the general. Raspberry Pi has been used to build the proposed intelligent mirror, linked to the digital world via the Internet. The mirror can communicate with the user through voice commands and reply appropriately. The monitoring of emotions and health measuring function will provide a distinctive experience to the users. The mirror will reflect important elements such as weather, date & time, covid-19 situation reports, local news, To-do list, water reminder, home workouts, and meal plans. The mirror can also handle specialized functions such as automating and controlling home IoT devices. © 2021 IEEE.

Using industrial 4.0 technologies to combat the COVID-19 pandemic.

The COVID 19 (Coronavirus) pandemic has led to a surge in the demand for healthcare devices, pre-cautions, or medicines along with advanced information technology. It has become a global mission to control the Coronavirus to prevent the death of innocent people. The fourth industrial revolution (I4.0) is a new approach to thinking that is proposed across a wide range of industries and services to achieve greater success and quality of life. Several initiatives associated with industry 4.0 are expected to make a difference in the fight against COVID-19. Implementing I4.0 components effectively could lead to a reduction in barriers between patients and healthcare workers and could result in improved communication between them. The present study aims to review the components of I4.0 and related tools used to combat the Coronavirus. This article highlights the benefits of each component of the I4.0, which is useful in controlling the spread of COVID-19. From the present study, it is stated that I4.0 technologies could provide an effective solution to deal with local as well as global medical crises in an innovative way.

Corona Warrior Smart Band

Background An outbreak of 2019 coronavirus pandemic (COVID-19) in urban centre, China, has unfolded quickly nationwide. There is a scarcity of human resource for observance of the symptoms of COVID-19 in patients or suspected person. Additionally, humans in the market for the monitorization of the symptoms are vulnerable because the COVID-19 will have an effect on those too. Methods Presently, there are many smartwatches/ smart bands available in the market which monitor the health parameters, but do not alert the user for any symptomatic changes in those parameters. And, the currently available watches/bands do not locate the person. Result This paper consists of an idea of inventing a smart healthcare device named as "Corona Warrior Smart Band" which measures the temperature, pulse rate and blood oxygen levels of a person without any human intervention and alerts the user and nearby COVID care centre (CCC) if any of the health parameters shows unusual observations in accordance with the COVID-19 conditions. This band also helps the COVID care centre's team to detect the live location of the suspected person. Conclusion This band is the best example of preventing the spreading and affection of COVID-19 virus in the society.

Autonomous Temperature scan system using IoT to detect COVID-19 symptom

The current situation of pandemic demands utmost care of health for every individual, rapid spread of SARS-CoV-2 needs regular temperature check-ups as one of the means of identifying the disease. The design of a low cost and efficient system to automate the human temperature sensing using IoT is presented in this paper. This system can be used in most of the places where temperature check ups are to be done and still using the manual check-up tools hence easing the way of checking the temperature. The system mainly consists of two subsystems. One subsystem that determines the temperature value (TS) and the other that triggers the temperature sensor (PS). The PS uses a proximity sensor that senses whether the person is near the temperature sensor and triggers the TS to sense the temperature of individual standing in front of it. The system can be mounted on any simple mirror that enables the individuals to align their head correctly with the temperature sensor and hence it can be used anywhere. To govern the behavior of the sensors we use a microcontroller (Node MCU). In this proposed method we are developing a cost-effective solution to detect the temperature of the individual without human resources installed in the place. The system can be used in various places such as Schools and colleges, public places, hospitals and many more. © 2021 IEEE.

OSA Patient Monitoring Based on the Beidou System.

This paper presents an OSA patient interactive monitoring system based on the Beidou system. This system allows OSA patients to get timely rescue when they become sleepy outside. Because the Beidou position marker has an interactive function, it can reduce the anxiety of the patient while waiting for the rescue. At the same time, if a friend helps the OSA patients to call the doctor, the friend can also report the patient's condition in time. This system uses the popular IoT framework. At the bottom is the data acquisition layer, which uses wearable sensors to collect vital signs from patients, with a focus on ECG and SpO2 signals. The middle layer is the network layer that transmits the collected physiological signals to the Beidou indicator using the Bluetooth Low Energy (BLE) protocol. The top layer is the application layer, and the application layer uses the mature rescue interactive platform of Beidou. The Beidou system was developed by China itself, the main coverage of the satellite is in Asia, and is equipped with a high-density ground-based augmentation system. Therefore, the Beidou model improves the positioning accuracy and is equipped with a special communication satellite, which increases the short message interaction function. Therefore, patients can report disease progression in time while waiting for a rescue. After our simulation test, the effectiveness of the OSA patient rescue monitoring system based on the Beidou system and the positioning accuracy of OSA patients have been greatly improved. Especially when OSA patients work outdoors, the cell phone base station signal coverage is relatively weak. The satellite signal is well-covered, plus the SMS function of the Beidou indicator. Therefore, the system can be used to provide timely patient progress and provide data support for the medical rescue team to provide a more accurate rescue plan. After a comparative trial, the rescue rate of OSA patients using the detection device of this system was increased by 15 percentage points compared with the rescue rate using only GPS satellite phones.

Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network.

The coronavirus pandemic has affected people all over the world and posed a great challenge to international health systems. To aid early detection of coronavirus disease-2019 (COVID-19), this study proposes a real-time detection system based on the Internet of Things framework. The system collects real-time data from users to determine potential coronavirus cases, analyses treatment responses for people who have been treated, and accurately collects and analyses the datasets. Artificial intelligence-based algorithms are an alternative decision-making solution to extract valuable information from clinical data. This study develops a deep learning optimisation system that can work with imbalanced datasets to improve the classification of patients. A synthetic minority oversampling technique is applied to solve the problem of imbalance, and a recursive feature elimination algorithm is used to determine the most effective features. After data balance and extraction of features, the data are split into training and testing sets for validating all models. The experimental predictive results indicate good stability and compatibility of the models with the data, providing maximum accuracy of 98% and precision of 97%. Finally, the developed models are demonstrated to handle data bias and achieve high classification accuracy for patients with COVID-19. The findings of this study may be useful for healthcare organisations to properly prioritise assets.

IOT based automation of Hardware-In-Loop test bench

The setup of an automated hardware-in-the-loop test bench using the IOT's ethernet facility is proposed in this paper. The implementation of this embedded structure was critical in the COVID-19 scenario for sticking to social distancing guidelines. The key hardware used to automate the manual test benches for in-house development of this embedded solution is an Arduino UNO with an Ethernet shield.Automated test bench setups have benefits over manual test bench setups, such as shorter testing times and no manual effort. The results show that the proposed automated hardware-in-the-loop test bench setup was successfully implemented, with the added benefit of internet of things using an Arduino Uno ethernet shield. © 2021 IEEE.

IoT BASED MEDICAL ROBOT FOR ASSISTING PATIENTS OF CONTAGIOUS DISEASE

Recently it is observed that the research advancement is very significant in the field of deployable robots for healthcare sector. Considering the current pandemic situation where social distancing is must- healthcare robots can be deployed very easily to perform the manual task resulting in reduced human efforts. In this paper, the design and implementation of an affordable, versatile system is proposed. The system is IoT enabled to meet the current demand and it can be controlled from any platform. The prototype can follow through lines and recognize hospital beds, can autonomously distribute medicines, record patient vital signs, and also has obstacle avoidance capabilities. There's also a 5-DOF arm, which is controllable over wireless medium by hand gestures. The IoT website can control the robot, record medicines, and view patient vitals' data. This work is intended to leave an impact on the healthcare sector of developing countries. © 2021 IEEE.

Current state of diagnostic, screening and surveillance testing methods for COVID-19 from an analytical chemistry point of view.

Since December 2019, we have been in the battlefield with a new threat to the humanity known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this review, we describe the four main methods used for diagnosis, screening and/or surveillance of SARS-CoV-2: Real-time reverse transcription polymerase chain reaction (RT-PCR); chest computed tomography (CT); and different complementary alternatives developed in order to obtain rapid results, antigen and antibody detection. All of them compare the highlighting advantages and disadvantages from an analytical point of view. The gold standard method in terms of sensitivity and specificity is the RT-PCR. The different modifications propose to make it more rapid and applicable at point of care (POC) are also presented and discussed. CT images are limited to central hospitals. However, being combined with RT-PCR is the most robust and accurate way to confirm COVID-19 infection. Antibody tests, although unable to provide reliable results on the status of the infection, are suitable for carrying out maximum screening of the population in order to know the immune capacity. More recently, antigen tests, less sensitive than RT-PCR, have been authorized to determine in a quicker way whether the patient is infected at the time of analysis and without the need of specific instruments.