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In today's health care context, the application of the Internet of Things (IoT) offers suitability for doctors and patients as we can use them in many medical fields. So, we have emphasized the particular use of the IoT in medicine and health care, such as clinical devices management, medication management, clinical data management, distant medicin...
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... A new type of intelligent sports management system is proposed in [19], which is constructed by using information technology and human-computer interaction technology under artificial intelligence and combining it with deep learning technology. There is an organized review of healthcare management systems in [20] and enhancement of healthcare management systems through many of the latest IoT-oriented healthcare applications. ...
... The quality value under the corresponding index is taken as the vertical axis to show the change of the corresponding index value of the selected features in each algorithm within the range of ½10, 15, 20, 25, 30, 35, 40, 45, 50. Specifically, in Figures 2-6, we can intuitively observe the change of the optimal result of the comparison algorithm under the optimal parameter as the number of selected features increases and the quality comparison of each algorithm under the same dataset and the same indicator when the number of selected features is ½10, 15,20,25,30,35,40,45,50. As can be seen in Figure 2, under the hamming loss index, the curves of the suggested algorithm on all datasets are below the curves of the comparison algorithm, thus indicating the superiority of the suggested algorithm in the hamming loss index. ...
People often suffer from unpredictable injuries during physical exercise. One of the important reasons is the absence of a scientific sports health management system. Therefore, the construction of such a scientific and effective system has gradually attracted the attention of scholars, which is of great significance to realizing people’s scientific and personalized physical fitness. An intelligent sports health management system based on big data analysis and the Internet of things (IoT) is constructed to solve this problem. The system consists of the user, IoT, cloud, system analysis, evaluation, and data layers. Firstly, a new multilabel feature selection algorithm is proposed in the system analysis layer. The suggested multilabel feature selection algorithm maps the sample space to the label space through the L 21 norm. Then, the consistency of various topologies is guaranteed by combining with feature popularity so that the factors affecting user health can be better selected. Secondly, the experiment is compared with SCLS, SSFS, and six other multilabel feature selection algorithms in 6 classic medical multilabel datasets. Experimental results under five indexes show the effectiveness and superiority of the proposed feature selection algorithm. Finally, the feasibility of the proposed intelligent sports management system is analyzed.
... On the other hand, the Internet of Things (IoT) technologies [3,4] are being used in many contemporary application fields like video surveillance, weather monitoring, air pollution control, smart road traffic, in addition to e-Health. In IoT-based e-Health systems [5][6][7], personal devices or wearable IoT sensors are transmitting data concerning the state of patients (e.g., body temperature, heart rate, oxygen saturation, glycemia) continuously, to collection points which can be computer systems of networked physicians or hospitals. These data streams are heterogeneous in nature due to the use of a variety of remote sensors, involve structured, semistructured and unstructured data and, in some cases, may also include multimedia contents (e.g., videos, sounds, images). ...
e-Health IoT sensor data represent entities which are evolving over time, and several healthcare applications require keeping a full history of such data. Moreover, e-Health IoT data can be considered as Big Data and the JSON format is being considered as the best data format to represent Big Data and to facilitate their management, storage and exchange. However, there is no standard/consensual temporal model for e-Health IoT data, including JSON that lacks explicit support of time-varying data. Consequently, in order to manage Big Data histories and temporal e-Health IoT data, application developers have to proceed in an ad hoc manner. For that purpose, we propose in this chapter TJeH (Temporal JSON e-Health IoT data model), a temporal extension of the JSON data model, which supports the representation of temporal aspects of e-Health IoT data. Besides, and since TJeH is a logical model, in order to facilitate the work of both designers and database administrators of e-Health applications, we have also defined a graphical conceptual model for time-varying e-Health IoT data, named C-TJeH (Conceptual TJeH), associated to TJeH. C-TJeH makes easy the design of temporal JSON e-Health IoT data, with a conceptual model that provides a graphical user-friendly representation.
... 3 The significant paradigm of IoT-based smart healthcare offers mobility from hospitalized-centric to home-centric environment. 4 Typically, the Internet of Medical Things (IoMT) functioning as rudimentary aspects in healthcare applications. The IoMT comprises numerous smart sensors that are deployed in a real-time environment to examine the healthcare conditions of the patient. ...
In today's world, the advancement of telediagnostic equipment plays an essential role to monitor heart disease. The earlier diagnosis of heart disease proliferates the compatibility of treatment of patients and predominantly provides an expeditious diagnostic recommendation from clinical experts. However, the feature extraction is a major challenge for heart disease prediction where the high dimensional data increases the learning time for existing machine learning classifiers. In this article, a novel efficient Internet of Things-based tuned adaptive neuro-fuzzy inference system (TANFIS) classifier has been proposed for accurate prediction of heart disease. Here, the tuning parameters of the proposed TANFIS are optimized through Laplace Gaussian mutation-based moth flame optimization and grasshopper optimization algorithm. The simulation scenario can be carried out using11 different datasets from the UCI repository. The proposed method obtains an accuracy of 99.76% for heart disease prediction and it has been improved upto 5.4% as compared with existing algorithms.
Fitted with both physiological and biomechanical sensors, the devices track vital signs, motion performance monitoring, as well as contextual parameters important to patient health. Wearable devices for human augmentation include the exoskeletons and smart prosthetics that increase physical abilities, as well as cognitive computing widgets to improve brain function or sensory perception. Rehabilitation, chronic disease management, and preventive healthcare are also being revolutionized by technology, empowering people to take control of their health. Wearables have shown themselves fit for human augmentation by the right people.
The disruptive impact of Machine Learning presents an opportunity to rethink the optimization of industrial processes, especially in the complex supply chain. The need to reduce environmental effect is driving a paradigm change in the industrial sector towards sustainability. The world struggles associated with sustainable development, the manufacturing industry is leading the charge in pursuing efficiency and environmentally responsible methods. The revolutionary potential of the machine learning to revolutionize factory optimization especially in the supply chain is examined. This chapter focuses on the understanding of current challenges in manufacturing optimization for sustainability; explore the fundamentals of Machine Learning and it's application to manufacturing; analyze diverse aspects and examples of Machine Learning in supply chain optimization; discuss the potential impact of Machine Learning on sustainability within the manufacturing sector while reducing its environmental impact and advancing global sustainability.
Real-time data collection, monitoring and analysis of patient through sensors and wearable technologies are today transforming healthcare to reach a new level that was once unattainable. Meshing with IoT and AI programs, digital health tools can monitor akin to blood pressure, glucose levels heart rate or physical activity. Wearable devices such as Smart-watches, Fitness Trackers and Health Patches offer ways to non-invasively detect early disease states and also help manage some chronic diseases. In plant & human augmentation, such innovations assist in medical interventions covering prosthetics and exoskeletons resulting in mobility as well quality of life for disabled individuals. These human augmentation technologies are part of a mass ascertainment towards preventive healthcare, bridging the gap between patients and their care team, unifying an integrated-care continuum to create more connected operational workflows of all autonomously powered by patient-generated health data, catalyzing a seismic shift in digital health innovation.
Real-time monitoring, data-driven decision-making and improved patient care are all made possible by the integration of sensors and nanosensor networks in smart hospitals which is transforming the healthcare industry. Smart hospitals use cutting-edge technology to improve patient care, maximize operational effectiveness and support data-driven decision-making. They represent a paradigm change in the delivery of healthcare. Sensors are at the vanguard of this revolution which acting as the pivotal point in the assimilation of data-centric methods for healthcare administration. The revolutionary age in healthcare has begun with the development of sensors and nanosensor networks especially in the context of smart hospitals. It captures the essence of the various applications, difficulties and potential directions that using advanced sensing technology in the healthcare industry may take. To provide readers a thorough grasp of how sensors and nanosensor networks will affect healthcare in the future, the chapter synthesizes data and evaluates relevant literature. This chapter also examines the many uses, difficulties and potential uses of sensor technology in the context of smart hospitals.
Patient management, diagnosis, and medical care have all changed as a result of the incorporation of Intelligent Systems (IS) and the Internet of Things (IoT) into clinical healthcare. With a focus on real-time patient monitoring, predictive analytics, and customised treatment plans, this paper outlines the major ways in which these technologies might improve healthcare outcomes. AI and machine learning-powered intelligent systems handle large amounts of clinical data to provide risk assessment, early disease detection, and optimal treatment regimens. Through the direct transmission of vital signs and health indicators to healthcare practitioners for prompt intervention, Internet of Things (IoT) devices, such as wearables, implanted sensors, and smart medical equipment, provide continuous health monitoring. In clinical health, IS and IoT work together to minimise hospital admissions, promote proactive health care, especially for patients with chronic illnesses, and enable remote patient monitoring.
The development of very effective imaging systems with improved resolution for use by medical experts in real-time is promised by quantum computers. This covers developments like knowing how proteins fold, examining how medications and enzymes interact molecularly, and accelerating clinical trials. Personalized treatment options are made possible by quantum computers' fast DNA sequencing capabilities, especially in the fight against hereditary illnesses. Their accuracy and effectiveness make it possible to investigate novel treatment approaches. Quantum computing has enormous promise for pharmaceutical research and development since it can interpret and reproduce complex chemical and biological processes like never before. With pushing the frontiers of scientific innovation and discovery, the use of quantum computing to healthcare has the potential to improve patient care and expedite medical advancements. This chapter comprehensively explores the various dimensions of the quantum computing in health and medicines.
