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Human Muscle Measurement and Big Data Health Based on Wireless Sensors

机译：基于无线传感器的人类肌肉测量和大数据健康

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摘要

Research in computer science and medical applications such as wireless sensor technology is considered one of the most important fields in improving life quality. The purpose of this article is to provide an image of current trends and future directions for the study of continuous injection monitoring of patient wear and human lane systems. The human muscles' size to assess by ultrasound and Magnetic Resonance Imaging (MRI) in 6 subjects throughout the body. In both proposed methods (preprocessing, feature extraction, classification), 11 axial scans were taken with the abdominal muscles, and the cross-sectional area of each scan muscle digitized. Muscle mass is calculated by processing the muscles into continuous cones. Explain the important role of this program as a big data human health analysis in wireless sensor, not only in providing high-quality care to people but also in reducing the living needs of the elderly independently from the nursing staff and helping patients with chronic illness. In introducing to propose a comprehensive survey based on the Sensitivity Enhancement based Classification Algorithm (SEBCA) proposed in the most advanced research activities in the country to address this gap and recommended wireless sensor for the large data system. Potential applications, networks and infrastructure technology challenges work together and are explained in terms of research areas and objectives.
机译：无线传感器技术等计算机科学和医疗应用的研究被认为是提高生活质量的最重要领域之一。本文的目的是提供目前趋势和未来方向的图像，用于研究患者磨损和人道系统的连续注射监测。人体肌肉的大小通过超声波和磁共振成像（MRI）在整个身体的6个受试者中进行评估。在两个提出的方法（预处理，特征提取，分类）中，用腹部肌肉进行11个轴向扫描，以及数字化的每个扫描肌的横截面积。通过将肌肉加工成连续锥体来计算肌肉质量。解释该计划作为无线传感器的大数据人体健康分析的重要作用，不仅在为人们提供高质量的护理，而且还可以从护理人员独立地降低老年人的生活需求，并帮助慢性疾病患者。在介绍基于敏感性增强的敏感性增强的分类算法（SEBCA）中提出的全面调查，提出了该国最先进的研究活动，以解决大型数据系统的这种差距和推荐的无线传感器。潜在的应用，网络和基础设施技术挑战在一起，并在研究领域和目标方面解释。

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  • 来源
    《Microprocessors and microsystems》 |2021年第2期|103580.1-103580.7|共7页
  • 作者

    Han Kunjian; Zhang Lei; Tian Zhuang; Gu Yu; Wang Anjie;

  • 作者单位

    Jeonbuk Natl Univ Coll Educ Dept Phys Educ Jeonju Si 54896 Jeollabuk Do South Korea|Xinyang Agr & Forestry Univ Dept Phys Educ Xinyang 464000 Henan Peoples R China;

    Jeonbuk Natl Univ Coll Nat Sci Dept Sports Sci Jeonju Si 54896 Jeollabuk Do South Korea|Zhengzhou Univ Coll Phys Educ Main Campus Zhengzhou 450001 Henan Peoples R China;

    Jeonbuk Natl Univ Coll Nat Sci Dept Sports Sci Jeonju Si 54896 Jeollabuk Do South Korea|Zhengzhou Univ Coll Phys Educ Main Campus Zhengzhou 450001 Henan Peoples R China;

    Jeonbuk Natl Univ Coll Nat Sci Dept Sports Sci Jeonju Si 54896 Jeollabuk Do South Korea|Zhengzhou Univ Coll Phys Educ Main Campus Zhengzhou 450001 Henan Peoples R China;

    Jeonbuk Natl Univ Coll Educ Dept Phys Educ Jeonju Si 54896 Jeollabuk Do South Korea|Zhengzhou Univ Coll Phys Educ Main Campus Zhengzhou 450001 Henan Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Sensitivity Enhancement based Classification; Algorithm (SEBCA); Preprocessing; Feature extraction and selection; classification; Magnetic Resonance Imaging (MRI);

    机译：基于敏感性增强的分类;算法（SEBCA）;预处理;特征提取和选择;分类;磁共振成像（MRI）;

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