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The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.

This data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.

The proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.

The research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.

The emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.

In this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

The purpose of the research work is to focus on the deployment of wearable sensors in addressing symptom Analysis in the Internet of Things (IoT) environment to reduce human interaction in this epidemic circumstances.

COVID-19 pandemic has distracted the world into an unaccustomed situation in the recent past. The pandemic has pulled us toward data harnessing and focused on the digital framework to monitor the COVID-19 cases seriously, as there is an urge to detect the disease, wearable sensors aided in predicting the incidence of COVID-19. This COVID-19 has initiated many technologies like cloud computing, edge computing, IoT devices, artificial intelligence. The deployment of sensor devices has tremendously increased. Similarly, IoT applications have witnessed many innovations in addressing the COVID-19 crisis. State-of-the-art focuses on IoT factors and symptom features deploying wearable sensors for predicting the COVID-19 cases. The working model incorporates wearable devices, clinical therapy, monitoring the symptom, testing suspected cases and elements of IoT. The present research sermonizes on symptom analysis and risk factors that influence the coronavirus by acknowledging the respiration rate and oxygen saturation (SpO₂). Experiments were proposed to carry out with chi-Square distribution with independent measures t-Test.

IoT devices today play a vital role in analyzing COVID-19 cases effectively. The research work incorporates wearable sensors, human interpretation and Web server, statistical analysis with IoT factors, data management and clinical therapy. The research is initiated with data collection from wearable sensors, data retrieval from the cloud server, pre-processing and categorizing based on age and gender information. IoT devices contribute to tracking and monitoring the patients for prerequisites. The suspected cases are tested based on symptom factors such as temperature, oxygen level (SPO₂), respiratory rate variation and continuous investigation, and these demographic factors are taken for analyzed based on the gender and age factors of the collected data with the IoT factors thus presenting a cutting edge construction design in clinical trials.

The contemporary study comprehends 238 data through wearable sensors and transmitted through an IoT gateway to the cloud server. Few data are considered as outliers and discarded for analysis. Only 208 data are contemplated for statistical examination. These filtered data are proclaimed using chi-square distribution with t-test measure correlating the IoT factors. The research also interprets the demographic features that induce IoT factors using alpha and beta parameters showing the equal variance with the degree of freedom (df = 206).

The purpose of this study is to examine the use of IoT technology (RFID technology, sensor networks, wearable devices and other smart items) in office settings and its respective impact on the optimization of employees’ productivity and workspace effectiveness.

The paper reviews 41 relevant publications reporting IoT use in office settings to identify how this technology has been applied in office settings and what topics are mostly addressed in the literature; how IoT technology improves employees’ productivity; and what the benefits and risks associated with IoT use in the workplace environment are.

Two main areas of application of IoT technology in the workplace environment were identified. The first one concerns the influence of the physical characteristics of workplaces on aspects related to workspace effectiveness. The second one is employee-centered and concerns the use of IoT data to identify employees’ social behavior, physiological data and emotional estates associated with productivity. IoT technology provides real-time data with speedy information retrieval. However, its deployment in office settings is not exempt from risks. Employee workplace surveillance, re-individualization of the IoT data and employee refusal of IoT technology in office settings are the main risks associated with this technology.

This literature review categorizes IoT application in office settings according to two perspectives and highlights employees' attitudes, user-experience of IoT technology and the risks associated with this technology. These results will help researchers and workplace managers interested in the deployment of this technology in the workplace environment.

Coronavirus Disease 2019 (COVID-19) necessitated the need for “Hospital-at-home” improvisations that involve wearable technology to classify patients within households before visiting health institutions. Do-It-Yourself wearable devices allow for the collection of health data leading to the detection and/or prediction of the prevalence of the disease. The sensitive nature of health data requires safeguards to ensure patients’ privacy is not violated. The previous work utilized Hyperledger Fabric to verify transmitted data within Smart Homes, allowing for the possible implementation of legal restrictions through smart contracts in the future. This study aims to explore privacy-enhancing authentication schemes that are operated by multiple credential issuers and capable of integration into the Hyperledger ecosystem.

Design Science Research is the methodology that was used in this study. An architecture for ABC-privacy was developed and evaluated.

While the privacy-by-design architecture enhances data privacy through edge and fog computing architecture, there is a need to provide an additional privacy layer that limits the amount of data that patients disclose. Selective disclosure of credentials limits the number of information patients or devices divulge.

The evaluation of this study identified Coconut as the most suitable attribute-based credentials scheme for the Smart Homes Patients and Health Wearables use case Coconut user-centric architecture Hyperledger integration multi-party threshold authorities public and private attributes re-randomization and unlinkable revelation of selective attribute revelations.

Introduction: There is a variety of wearables and health applications available in the market which allow the tracking of various health and lifestyle measures like blood sugar, calorie counter, number of steps, sleep patterns, etc. After the Covid-19 pandemic, people have become more aware of their health and use these wearables to maintain a healthy lifestyle. Insurance companies in India are also eyeing the potential usage of these wearables in life and health insurance.

Purpose: This research aims to look at the emergence of wearables and health apps and their usage in India’s life and health insurance industry. This study also focuses on how these devices might benefit insurers’ business models and some of the pitfalls to consider.

Methodology: The study used both primary and secondary data. A survey was conducted to understand the customer perception towards usage of wearables. The secondary research included the analysis of the integration of wearables by insurance companies.

Findings: The research would be helpful to the insurance companies as it would help them to understand the customer’s viewpoint for the usage of wearables in the insurance industry. This study would also allow insurers to understand new dimensions, such as where the wearables improve customer satisfaction and engagement. The study results would be helpful for the customers for the appropriate usage of wearables and the internet of things (IoT). Insurance companies can provide better pricing and make personalised insurance plans that ultimately help customers.

This study aims to provide a systematic review about the Internet of Things (IoT) and its impacts on happiness. It intends to serve as a platform for further research as it is sparse in in-depth analysis.

This systematic review initially observed 2,501 literary articles through the ScienceDirect and WorldCat search engines before narrowing it down to 72 articles based on subject matter relevance in the abstract and keywords. Accounting for duplicates between search engines, the count was reduced to 66 articles. To finally narrow down all the literature used in this systematic review, 66 articles were given a critical readthrough. The count was finally reduced to 53 total articles used in this systematic review.

This paper necessitates the claim that IoT will likely impact many aspects of our everyday lives. Through the literature observed, it was found that IoT will have some significant and positive impacts on people's welfare and lives. The unprecedented nature of IoTs impacts on society should warrant further research moving forward.

While the literature presented in this systematic review shows that IoT can positively impact the perceived or explicit happiness of people, the amount of literature found to supplement this argument is still on the lower end. They also necessitate the need for both greater depth and variety in this field of research.

Since technology is already a pervasive element of most people’s contemporary lives, it stands to reason that the most important factors to consider will be in how we might benefit from IoT or, more notably, how IoT can enhance our levels of happiness. A significant implication is its ability to reduce the gap in happiness levels between urban and rural areas.

Currently, the literature directly tackling the quantification of IoTs perceived influence on happiness has yet to be truly discussed broadly. This systematic review serves as a starting point for further discussion in the subject matter. In addition, this paper may lead to a better understanding of the IoT technology and how we can best advance and adapt it to the benefits of the society.

The recent advancement in gait analysis combines internet of things that provides better observations of person living behavior. The biomechanical model used for elderly and physically challenged persons is related to gait-related parameters, and the accuracy of the existing systems significantly varies according to different person abilities and their challenges. The paper aims to discuss these issues.

Deployment of wearable sensors in gait analysis provides a better solution while tracking the changes of the personal style, and this proposed model uses an electronics system using force sensing resistor and body sensors.

Experimental results provide an average gait recognition of 95 percent compared to the existing neural network-based gait analysis model based on the walking speeds and threshold values.

The sensors are used to monitor and update the predicted values of a person for analysis. Using IoT a communication process is performed in the research work by identifying a physically challenged person even in crowded areas.

Construction safety is heavily affected by using new technologies in this growing trend of technology adoption. Especially, safety performance is enhanced through the utilization of some effective technologies such as artificial intelligence, virtual reality, BIM and wearable devices. Therefore, the main purpose of this study is to investigate the influence of emerging technologies on construction safety performance and quantify the relationship between those. The proposed components of emerging technologies are BIM, GIS, VR, RFID, AI, ML, eye tracking and serious games and wearable devices, whereas the dimensions of construction safety performance are safety planning, safety training, safety inspection and monitoring, safety audits and reviews and safety leadership.

A structural model was composed consisting of emerging technologies and safety performance indicators. Then, a questionnaire was designed and administered to construction professionals, and data from 167 projects were analyzed using structural equation modeling. The data were analyzed by using software, called SPSS AMOS.

The analysis of the structural model proves that there is a positive and significant relationship between emerging technologies and construction safety performance. Moreover, the factor loadings for each factor were found to be high indicating a good representation of the construct by the components developed. Among the technologies, BIM, robotics and automation, AI and wearable devices were detected to be the most significant technologies in terms of impacting safety performance.

The study contributes to the body of knowledge in that it develops a conceptual framework consisting of specific technologies in terms of emerging technologies, reveals the impact of such technologies on safety performance and proposes several tools and strategies for enabling effective safety management along the project lifecycle. Industry practitioners may benefit from the framework developed by adopting such technologies to enhance their safety performance on construction projects.

The situations of COVID-19 will certainly have an adverse effect over and above health care on factors of the internet of things (IoT) market. To overcome all the above issues, IoT devices and sensors can be used to track and monitor the movement of the people, so that necessary actions can be taken to prevent the spread of coronavirus disease (COVID-19). Mobile devices can be used for contact tracing of the affected person by analyzing the geomap of the travel history. This will prevent the spread and reset the economy to the normal condition.

To respond to the global COVID-19 outbreak, the social-economic implications of COVID-19 on specific dimensions of the global economy are analyzed in this study. The situations of COVID-19 will certainly have an adverse effect over and above health care on factors of the IoT market. To overcome these issues IoT devices and sensors can be used to track and monitor the movement of the people so that necessary actions can be taken to prevent the spread of COVID-19. Mobile devices can be used for contact tracing of the affected person by analyzing the geomap of the travel history. This will prevent the spread and reset the economy to the normal condition. A few reviews, approaches, and guidelines are provided in this article along these lines. Moreover, insights about the effects of the pandemic on various sectors such as agriculture, medical industry, finance, information technology, manufacturing and many others are provided. These insights may support strategic decision making and policy framing activities for the top level management in private and government sectors.

With insecurities of a new recession and economic crisis, key moments such as these call for strong and powerful governance in health, business, government, and large society. Instant support measures have to be initiated and adapted for those who can drop through the cracks. Mid- and long-term strategies are required to stabilize and motivate the economy during this recession.

A comprehensive social-economic development strategy that consists of sector by sector schemes and infrastructure that supports business to ensure the success of those with reliable and sustainable business models is necessary. From the literature analysis and real world observations it is concluded that the IoT, sensors, wearable devices and computational technologies plays major role in preserving the economy of the country by preventing the spread of COVID-19.

In the ongoing Industry 4.0 era, the internet of things (IoT) has become a global race in the current information technology climate. However, little is understood about the pattern of the global competitive arena or its players’ set up strategy. This paper aims to attempt to compare the cross-country development of the IoT industry. In particular, from the lens of industrial policies, this paper highlights how China, as a latecomer, gains momentum to emerge victorious as a leader in this global race.

Based on five dimensions, namely, foundation, trajectory, characteristic, application and social impacts, this paper presents the evolution of the IoT industry in the USA, European Union, Japan, South Korea and China. From the lens of windows of opportunities, this paper analyzes how China seized the opportunity with the emerging technology, thereby, enabling it to create a competitive advantage.

This paper finds that China’s IoT industry takes a distinct trajectory, where scientific institutions, enterprises and governmental policies collaborate in unison, during which the first phase was when scientific research institutions introduced the conceptual new technology from developed countries. This technological foresight allowed for the identification and realization of critical technologies, strategic fields and technological trends. The second phase was the continuous dissatisfaction of capabilities of critical technologies, which creates disruptions that significantly altered the environment of technological competition.

This paper provides a comprehensive and comparative review of IoT industries in a global context, with the critical and influential role of the windows of opportunities on those enterprises lagging behind the technological wave.