Research On Body Fat Measurement Method And System Based On Bioelectrical Impedance

With the rapid development of the national economy and the continuous improvement of the quality of life of the people,the dietary habits of our people are constantly changing.Many high-sugar,high-fat,high-calorie foods have entered the table of the people,and many such as milk tea High-calorie drinks are loved by young people.However,such eating habits have also led to a continuous rise in the rate of obesity in the society.Poor diet can also bring many chronic diseases caused by obesity,such as hypertension and hyperlipidemia.In China,the proportion of middle-aged and elderly people suffering from hypertension and hyperlipidemia has risen sharply,and due to irregular lifestyles and poor eating habits,more and more young people also suffer from hypertension.If you can monitor the body fat rate in real time and prevent it,you can effectively reduce the incidence of these diseases.Based on the above background,this article designs and develops a low-cost and easy-to-operate body fat percentage detection system that can be used daily in the home,which can realize real-time detection of the body fat percentage.This paper uses Ohm’s law as the theoretical basis,uses the bioimpedance method,establishes a non-invasive body fat rate detection system model,and proposes a new type of limit learning machine(PSO-ELM)model optimized using particle swarm optimization.Body fat rate prediction algorithm.Consult the previous literature on body fat ratio research and human bioelectrical impedance detection methods,establish human impedance equivalent circuits,and design human bioelectrical impedance detection algorithms based on orthogonal demodulation algorithms.The AFE4300 chip produced by TI company was selected to design and develop the impedance signal acquisition and weight information acquisition front-end module,and the STM32ZET6 single-chip microcomputer was used as the control chip of this system.Finally,the wireless data transmission with the upper computer was realized through the network module,completing the whole The hardware design of the fat detection system.The impedance measurement front-end module transfers the impedance signal transmission value into the single-chip microcomputer through SPI communication,and imports it into the designed Kalman filter model for digital filtering to obtain stable and reliable impedance information.Then,the impedance information is transmitted to the upper computer through the network module,and the external physiological parameters such as the weight and height of the human body are collected to form a feature matrix.The least squares algorithm is used to fit the body fat rate prediction equation,and the PSO-ELM algorithm is proposed to construct the bioelectrical impedance-body fat rate prediction model,and the traditional limit learning machine is used Controlled trials.