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A wearable expert system (WES) is an expert system designed and implemented to obtain input from and give outputs to wearable devices. Among its distinguishing features are the direct cooperation between domain experts and users, and the interaction with a knowledge maintenance system devoted to dynamically update the knowledge base taking care of the evolving scenario. The paper aims to discuss these issues.

The WES development method is based on the Knowledge Acquisition Framework based on Knowledge Artifact (KAFKA) framework. KAFKA employs multiple knowledge artifacts, each devoted to the acquisition and management of a specific kind of knowledge. The KAFKA framework is introduced from both the conceptual and computational points of view. An example is given which demonstrates the interaction, within this framework, of taxonomies, Bayesian networks and rule-based systems. An experimental assessment of the framework usability is also given.

The most interesting characteristic of WESs is their capability to evolve over time, due both to the measurement of new values for input variables and to the detection of new input events, that can be used to modify, extend and maintain knowledge bases and to represent domains characterized by variability over time.

WES is a new and challenging concept, dealing with the possibility for a user to develop his/her own decision support systems and update them according to new events when they arise from the environment. The system fully supports domain experts and users with no particular skills in knowledge engineering methodologies, to create, maintain and exploit their expert systems, everywhere and when necessary.

The purpose of this paper is to present a framework for cultivating virtual communities of practice in distributed environments. The framework is based on the integration of knowledge artifacts and wearable technologies.

The proposed knowledge artifact is based on the correlation between conceptual and computational tools for the representation of different kinds of knowledge.

In this way, it is possible to make deeper the collaboration between knowledge seekers and contributors within the community, given that seekers and contributors share, at least in part, design choices at the knowledge modeling level.

A practical application of the framework has been described, to show its originality with respect to traditional knowledge management systems. In particular, it has been demonstrated how lurking phenomenon inside communities of practice can be significantly reduced. To this aim, opportune indexes have been defined from existing ones in literature.

This study aims to explore the impact of the adoption of wearable technology products for Indian health insurance firms. It identifies the key dynamic capabilities that health insurance firms should build to manage big data generated by wearable technology so as to attain a competitive advantage.

A qualitative exploratory study using in-depth personal interviews with 53 Indian health insurance experts was conducted with a semi-structured questionnaire. The data were coded using holistic and pattern codes and then analyzed using the content analysis technique. The findings were based on the thematic and relational intensity analysis of the codes.

An empirical model was established where all the propositions were strongly established except for the moderate relationship between wearable technology adoption and product innovation. The study established the nature of the interaction of variables on technology policy, organizational culture, strategic philosophy, product innovation, knowledge management and customer service quality with wearable technology adoption and also ascertained its influence on firm performance and competitive advantage.

From a dynamic capabilities perspective, this study deliberates on wearable technology adoption in the health insurance context. It also explicates the relationship between the variables on technology policy, organizational culture, strategic philosophy, product innovation, knowledge management and customer service quality with wearable technology adoption on firm performance.

This study is one of the first studies to add the context of wearable technology and health insurance to the existing body of knowledge on dynamic capabilities and sustainable competitive advantage for the service sector. It would help existing and prospective players in adopting or setting up appropriate business models.

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.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

The purpose of this study is to examine diverse risks and barriers that influence customers' attitude leading to their actual use of wearable devices in India. This study used technological literacy as a moderating variable to understand the relationship between barriers and attitudes toward adoption of wearable device.

A survey questionnaire was developed through focused group discussions with field experts. Data were collected through online as well as offline modes. A Google form was created and its weblink was shared with the respondents using wearable devices. Both online as well as offline modes were used for data collection. Several reminders through telephone and revisits were undertaken to approach the respondents.

The results of this study indicated that psychological risk and financial risk emerged strongest barriers of wearable technologies. This was followed by infrastructure barriers and performance risk. The strength of the relationship between technological anxiety and attitudes was lower but still significant. Surprisingly, privacy risk and social risk were not statistically significant. This study also validated the impact of technological literacy as a moderator between risks and attitudes.

This study contributes to the research by validating numerous risks and barriers in the adoption of wearable devices. This study not only offers a novel perspective on researching diverse barriers but also elucidates the moderating role of technological literacy which has not been covered in extant literature.

The purpose of the study was to empirically validate the linkages between IoT adoption and how it overarched influenced the patient care service engagement. This contributes to the body of knowledge and helps hospital managers to understand the relationship and relevance of IoT adoption; otherwise healthcare sector are late movers towards technology adoption. This gives a nuanced framework towards establishing empirically validated framework which will motivate healthcare services providers to be motivated to adopt and implement IoT enabled care delivery. The physician patient interaction and alignment during decision making will foster positive word of mouth, superior care service and reduce extra overheads for healthcare providers without compromise or rather with increment in service delivery proposition.

The study theoretically and empirically describes that with the adoption of internet of things (IoT) devices in health care, better services can be provided to patients by using partial least square – structure equation modelling-based robust technique and explains the better understanding of the health-care process with the help of information pervasiveness, physician-patient orientation and improved patient and physician involvement in the decision-making process.

This study shows that wearable IoT device adoption in health-care service delivery opens new opportunities and disrupts the conventional and traditional way of health-care service delivery by empowering the patient to take part in decision-making and enhancing their engagement in health-care service delivery.

The study might influence by generalizability. Perception-based cross-examination knowledge from the patient’s perspective. It is likely that patients who use these devices will grow accustomed to using them and become more capable of using them. Thus, time-series tests have not been used to catch enhanced skills. New patients’ experiences will be altered over time. Regardless, non-response bias and traditional process bias received excessive interest.

The study aims at unravelling how the adoption of IoT enabled practices and usage of IoT devices bolsters the available data points in the context of healthcare especially with respect to patient care delivery. The study conceptualizes and empirically validates how the usage of IoT interface enabled technology enables better patient treatment and caregiver participation. The study puts forth a nuanced understanding regarding how pervasively available ubiquitous care information fosters shared decision making. This study further emphasizes that importance of ensuring a reliable computing environment devoid of privacy and security risks. The study attempts at Emphasizing empirically how the enhanced information pervasiveness catapults the patient-provider interactions, through health data exchange. Highlighting the importance of search feature in cloud storage and recovery mechanisms. The study not only fulfills the overarching linkage between enhanced service engagement with IoT adoption, it provides a mental map and ready to refer framework for hospital and healthcare experts to refer to, which prescribes thar care providers must build new methods aimed at empowerment of patients to participate and take more inclusive role. This unique confluence between patients and physicians will unravel the sync; helping not only avoid costly decision errors, but also improve patient care delivery environment. Patients should be permitted to participate in decision-making,inspire patients to be participatory.

The study efforts to empirically investigate and discover the link between how wearable sensor-based IoT enhances health-care service engagement is underway. Using primary data this linkage validation allows the community and readers at large to gain a nuanced understanding of how superior interaction is enabled by a digital-health-care process with the help of IoT-enabled information pervasiveness, physician-patient orientation and empowered involvement.

Recent developments in wearable technologies have paved the way for continuous monitoring of the electrocardiogram (ECG) signal, without the need for any laboratory settings. A number of wearable sensors ranging from wet electrode sensors to dry sensors, textile-based sensors, knitted integrated sensors (KIS) and planar fashionable circuit boards are used in ECG measurement. The purpose of this study is to carry out a comparative study of the different sensors used for ECG measurements. The current challenges faced in developing wearable ECG sensors are also reviewed.

This study carries out a comparative analysis of different wearable ECG sensors on the basis of four important aspects: materials and methods used to develop the sensors, working principle, implementation and performance. Each of the aspects has been reviewed with regard to the main types of wearable ECG sensors available.

A comparative study of the sensors helps understand the differences in their operating principles. While some sensors may have a higher efficiency, the others might ensure more user comfort. It is important to strike the right balance between the various aspects influencing the sensor performance.

Wearable ECG sensors have revolutionized the world of ambulatory ECG monitoring and helped in the treatment of many cardiovascular diseases. A comparative study of the available technologies will help both doctors and researchers gain an understanding of the shortcomings in the existing systems.

Examines the fifthteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Examines the fifteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.