RETRACTED: Human muscle measurement and health management based on FPGA and machine learning

Abstract

Abstract 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.