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首页> 外文期刊>Microprocessors and microsystems >Human Muscle Mass Measurement through passive Flexible UWB-Myogram Antenna sensor to diagnose Sarcopenia
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Human Muscle Mass Measurement through passive Flexible UWB-Myogram Antenna sensor to diagnose Sarcopenia

机译：通过被动柔性UWB-MOMACON天线传感器进行人体肌肉质量测量，以诊断SARCOPENIA

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摘要

Sarcopenia disease is due to low muscle mass in humans. Sarcopenia leads to osteoporosis, metabolic syndrome and difficulty in performing day-to-day activities. At present, Dual-energy X-ray Absorptiometry (DXA) measures muscle mass with few limitations. They are variations in measurements according to region under investigation, irregularities in hydration status, and low precision in tall and obese persons. These limitations are due to low dosage level of X-ray radiations in certain muscle regions of human body such as heart, head, lower and upper extremities. This paper presents a non-invasive passive flexible Ultra Wide Band (UWB) Myogram antenna sensor for the prediction of Sarcopenia through human muscle mass measurement. This antenna is adhesively fixed on ventral surface of forearm and biceps for the measurement of skeletal and lean mass respectively. The proposed antenna sensor performs electromagnetic energy absorption from muscle tissues under radiating near-field condition. The muscle tissue signal from antenna is applied to blind source filtering-Non-negative Matrix Factorization (NMF), then subjected to Multi-Synchm Squeezing Transform (MSST), and finally correlated using linear regression machine learning algorithm to diagnose Sarcopenia. Furthermore, the proposed methodology is developed as a product through the MATLAB Mobile App compatible with Android devices. The proposed method of diagnosing Sarcopenia achieves an accuracy of 85% in fifty samples.
机译：Sarcopenia疾病是由于人类的肌肉低位。 Sarcopenia导致骨质疏松症，代谢综合征和表演日常活动的困难。目前，双能X射线吸收测定法（DXA）测量肌肉质量少量限制。它们是根据调查区域的测量的变化，水合状态的不规则性，高高的人和肥胖的低精度。这些限制是由于人体某些肌肉区域的X射线辐射的低剂量水平，例如心脏，头部，下肢和上末端。本文介绍了一种非侵入式无源柔性超宽带（UWB）MIMACON天线天线，用于通过人体肌肉质量测量预测SARCOPENIA。该天线在前臂和二头肌的腹表面上粘附，分别用于测量骨骼和贫物质。所提出的天线传感器从辐射近场条件下的肌肉组织进行电磁能量吸收。来自天线的肌肉组织信号被应用于盲源滤波 - 非负矩阵分解（NMF），然后经受多同步挤压变换（MSST），最后使用线性回归机学习算法相关来诊断SARCOPENIA。此外，所提出的方法是通过与Android设备兼容的Matlab移动应用程序作为产品开发的。所提出的诊断方法的诊断方法在50个样品中实现了85％的准确性。

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