Cross-sectional area muscle aspect ratio is one of the most widely used parameters for quantifying muscle function in both diagnostic and rehabilitation assessments. Ultrasound imaging has often been used to study the properties of human muscles as a non-invasive and reliable method. However, aspect ratio measurements are traditional FPGAs by manually digitizing the reference point. Therefore, it is subjectively time consuming and error prone. Muscle volume in the human region estimated in vivo by magnetic resonance imaging in six subjects by ultrasound. In both methods, machine learning takes along the muscle belly and cross-sectional area of the muscle in each scan, digitization. Muscle mass was calculated by processing the muscle as a series of truncated cones. To assess the reproducibility of the FPGA method, The factor of privacy In the sense that the test measure muscle strength in a particular sport by identifying the muscle groups that do the work and the same sports movement and maintain the same speed as possible, and the method of information collection and analysis should be easy and fast.

在诊断和康复评估中，截面积的肌肉纵横比是量化肌肉功能最广泛使用的参数之一。超声成像通常被用作研究人类肌肉特性的一种非侵入性且可靠的方法。但是，长宽比测量是传统的FPGA，通过手动数字化参考点来实现。因此，这在主观上很耗时并且容易出错。通过超声成像在六个受试者中通过磁共振成像在体内估计的人区域中的肌肉体积。在这两种方法中，机器学习都会在每次扫描（数字化）时沿着肌肉的腹部和肌肉的横截面积。通过将肌肉加工成一系列截头圆锥体来计算肌肉质量。为了评估FPGA方法的可重复性，