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      Design and Analysis of Adolescent Physical Health Monitoring System under the Background of Internet of Things and 5G

      research-article
      Journal of Healthcare Engineering
      Hindawi

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          Abstract

          In recent years, with the rapid development of network communication, 5G technology has brought about higher network speed and more communication requirements. Internet of Things technology has been widely used in many fields. Therefore, this paper realizes the physical health monitoring system under the Internet of Things and 5G communication technology to detect the physical health of teenagers in real time. Based on the in-depth analysis of the theoretical research and application status of the healthy Internet of Things at home and abroad, this paper first studies the theories and methods of teenagers' physical health information collection, collects human body temperature and movement steps, respectively, through wearable devices based on RFID anticollision algorithm, and further estimates human health and movement. The prototype system of the adolescent physical health monitoring system is realized. The system is divided into the terminal node and client information management system: (1) The terminal node is divided into information collection node and information collection node. The hardware circuit of the collection node mainly includes a microcontroller module, a sensor module, and a power circuit, and the collection node mainly includes a microcontroller module, a serial port module, and a power circuit. (2) The client information management system is divided into PC end and the mobile end. The PC end uses asp.net and SQL Server technology to design the database and user interface, and the mobile end uses WeChat public platform for development and design.

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          Most cited references16

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          IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning

          For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients' lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.
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            An XGBoost-based physical fitness evaluation model using advanced feature selection and Bayesian hyper-parameter optimization for wearable running monitoring

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              Sports and health big data system based on 5G network and Internet of Things system

              Kai Zhan (2021)
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                Author and article information

                Contributors
                Journal
                J Healthc Eng
                J Healthc Eng
                JHE
                Journal of Healthcare Engineering
                Hindawi
                2040-2295
                2040-2309
                2021
                15 November 2021
                : 2021
                : 5208976
                Affiliations
                School of Physical Education, Anhui Polytechnic University, Wuhu 241000, China
                Author notes

                Academic Editor: Fazlullah Khan

                Author information
                https://orcid.org/0000-0003-1520-8376
                Article
                10.1155/2021/5208976
                8608494
                34820076
                4fec9db4-9b9a-4374-ae92-e15bdbd88aad
                Copyright © 2021 Zhi Chen.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 3 September 2021
                : 27 October 2021
                Funding
                Funded by: Anhui Polytechnic University
                Award ID: KZ42020230
                Categories
                Research Article

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