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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.
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.
The disruptive impact of quantum computing presents an opportunity to rethink the optimization of industrial processes, especially in the complex supply chain. The need to reduce environmental effects is driving a paradigm change in the industrial sector towards sustainability. As 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 quantum computing to revolutionize factory optimization, especially in the supply chain, is examined. Quantum computing promises to solve challenging logistical challenges by utilizing the laws of quantum physics. The use of quantum computing in factory optimization offers enormous potential for a more environmentally friendly and sustainable future as it develops. So, accepting the quantum leap in technology may help the industrial sector reach previously unheard-of levels of productivity while reducing its environmental impact and advancing global sustainability.
Wearable sensors and the internet of things (IoT) have catalyzed a paradigm shift in the domain of medical and digital healthcare, particularly in the context of real-time scenarios. Wearable sensors have evolved to encompass a variety of types, from biosensors to motion and environmental sensors, enabling continuous and non-invasive data collection. IoT serves as the bridge that connects these sensors to healthcare systems, facilitating the seamless transmission and analysis of real-time data. Real-time data collection and analysis hold paramount significance, enabling timely medical decisions and interventions. It casts a visionary gaze into the future, exploring the integration of artificial intelligence and machine learning into the ecosystem, envisaging a landscape enriched by predictive modeling and enhanced real-time analysis. This chapter throws light into the intricacies of this convergence, elucidating how the assimilation of wearable sensors with IoT technologies is reshaping healthcare practices, elevating patient care, and redefining the boundaries of medical intervention.
Health is an indicator of a person's physiological and mental health which not only depicts a lack of illness but also defines the growth of that person (Huang et al. in International Journal of Communication Systems 34(4):e4683, [1). Keeping track of our health status regularly is inconvenient because of the strenuous schedules in our daily life. This system not only captures bio-signals like ECG which is a prime indicator of heart condition, by using AD8232, but also monitors other needed parameters such as SpO2 level and pulse rate by using MAX30102 and body temperature by using DS18B20. It also provides a maintained atmosphere like hospitals, by monitoring environment parameters like temperature and humidity level by using a DHT11 sensor. It uses an ESP32 microcontroller for Wi-Fi purposes; here it is used as a station point. This system does real-time monitoring of parameters of cardiac patients, then data is uploaded to the Cayenne cloud which provides data visualization, storing, and alerting. This system also sends a notification if an alarming condition occurs. When any of the parameters goes above the typical range, it sends an alert via mail or SMS to the registered mail or mobile number to notify the user or user family.
Healthcare is the foremost concern for any country. UN has also fixed ‘good health’ and well‐being’ as its third sustainable development goal. CoVID‐19 era has shown the vulnerability of health infrastructure worldwide. As per WHO, aging population of the world would put enormous pressure on health infrastructure in coming decades. With lack of healthcare professionals and limited budget to achieve UNSDG goal‐3, IoMT with its huge umbrella of medical services and applications can address these issues. 5G has been deployed in many countries and vision for 6G have been finalized by different research groups. 6G will help Internet of Medical Things to realize its full potential. In this work, an open ‘6G enabled IoMT’ architecture has been presented. It can integrate a wide number of services. The limitations of 5G in catering to such a network are also highlighted. The challenges and open issues are presented in the light of services and applications that can be provided by the purposed architecture. A brief overview of 6G is also provided.
Several industries have adopted Internet of Things (IoT) technology extensively. Several impediments have impeded its widespread adoption in the oil and gas industry, making it challenging to supervise the safety of infrastructure projects involving these industries. The objective of this study was to catalogue and analyze the barriers to using IoT for ensuring safety on oil and gas development sites. The primary objective of the study was to develop a model of the barriers preventing the use of IoT for oil and gas safety management. Using a mixed-methods research strategy, the data collection and analysis were carried out. The survey information was analyzed using structural equation modelling (SEM) and exploratory factor analysis (EFA). Five formative constructs emerged from the EFA and were confirmed by structural equation modelling: technical, organizational , integration, economic, and efficiency. Using SEM's path analysis, we determined that all five structures have a substantial impact on how oil and gas companies manage the safety of construction sites when utilizing IoT. The findings of this study could inform efforts to improve the safety management of oil and gas construction sites through IoT. Using the framework presented in this study, organizations in the gas and energy industry can eliminate the barriers to IoT deployment in building safety management.
According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.
