Research on Intelligent Abnormal Body Weight Monitoring Algorithm Based on Fat Depth Learning

At present, body fat and body weight measurement is only a single measurement, which can not achieve long-term and continuous health monitoring. There are some limitations in obesity prevention. The intelligent monitoring algorithm of body fat and body weight is designed based on deep learning. Human intracellular fluid and extracellular fluid belong to electrolytes. The cell membrane is equivalent to a capacitor to establish the equivalent model of human impedance. The impedance signal is collected, and the bioelectrical impedance of the human body is obtained by using the four electrode method. Combined with the variables such as height, age and gender, the content of human body components in the whole body of the subject is predicted. Calculate the body fat content and establish a complete body fat and weight data set. Input various human parameters related to body fat and body weight into the deep learning model for training, and the output prediction result is the intelligent monitoring result of body fat and body weight. The experimental results show that the intelligent monitoring algorithm of body fat and body weight based on deep learning can reduce the prediction error and effectively improve the monitoring performance. It has great credibility in the quantitative study of body fat rate.