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Human Muscle Mass Measurement through passive Flexible UWB-Myogram Antenna sensor to diagnose Sarcopenia
Microprocessors and Microsystems ( IF 1.9 ) Pub Date : 2020-09-24 , DOI: 10.1016/j.micpro.2020.103284
S. Sesha Vidhya , S. Rukmani Devi , K.G. Shanthi

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-Synchro 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.



中文翻译:

通过被动柔性UWB-Myogram天线传感器进行人体肌肉质量测量以诊断肌肉减少症

肌肉减少症是由于人的肌肉质量低引起的。肌肉减少症会导致骨质疏松症,代谢综合征,并且难以进行日常活动。目前,双能X线骨密度仪(DXA)测量肌肉质量几乎没有限制。它们是根据所调查区域的测量值变化,水合状态不规则以及高和肥胖者的精度低下的结果。这些限制是由于人体某些肌肉区域(例如心脏,头部,下肢和上肢)中X射线辐射的剂量水平较低。本文提出了一种非侵入式的被动柔性超宽带(UWB)Myogram天线传感器,用于通过人体肌肉质量测量预测肌肉减少症。该天线固定在前臂和二头肌的腹侧表面,分别用于测量骨骼和瘦体重。所提出的天线传感器在辐射近场条件下从肌肉组织吸收电磁能。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),然后进行多同步压缩变换(MSST),最后使用线性回归机器学习算法进行关联以诊断肌肉减少症。此外,通过与Android设备兼容的MATLAB Mobile App将提出的方法开发为一种产品。所提出的诊断肌肉减少症的方法在五十个样本中可达到85%的准确度。所提出的天线传感器在辐射近场条件下从肌肉组织吸收电磁能。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),然后进行多同步压缩变换(MSST),最后使用线性回归机器学习算法进行关联以诊断肌肉减少症。此外,通过与Android设备兼容的MATLAB Mobile App将提出的方法开发为一种产品。所提出的诊断肌肉减少症的方法在五十个样本中可达到85%的准确度。所提出的天线传感器在辐射近场条件下从肌肉组织吸收电磁能。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),然后进行多同步压缩变换(MSST),最后使用线性回归机器学习算法进行关联以诊断肌肉减少症。此外,通过与Android设备兼容的MATLAB Mobile App将提出的方法开发为一种产品。所提出的诊断肌肉减少症的方法在五十个样本中可达到85%的准确度。最后使用线性回归机器学习算法进行关联以诊断肌肉减少症。此外,通过与Android设备兼容的MATLAB Mobile App将提出的方法开发为一种产品。所提出的诊断肌肉减少症的方法在五十个样本中可达到85%的准确度。最后使用线性回归机器学习算法进行关联以诊断肌肉减少症。此外,通过与Android设备兼容的MATLAB Mobile App将提出的方法开发为一种产品。所提出的诊断肌肉减少症的方法在五十个样本中可达到85%的准确度。

更新日期:2020-10-05
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