Research on Bioelectrical Impedance and Body Fat Rate Prediction Model Based on PSO-ELM Algorithm in Sports Body Fat Monitoring System
In order to monitor the body fat rate of athletes in real-time and make preventive and reasonable exercise training ar-rangements,a bioimpedance detection method based on Kalman filter algorithm was studied and designed.A bioimpedance body fat rate prediction model and exercise fitness monitoring system were proposed based on Particle Swarm Optimization Extreme Learning Machine(PSO-ELM).The research results show that the maximum error of the human body electrical impedance signal processed by the Kalman filtering algorithm does not exceed±0.01.The difference in impedance values before and after the data is very small un-der conditions of skin dryness,water intake,and time.In comparison with mainstream prediction models,the maximum correlation coefficient of the PSO-ELM based bioelectrical impedance body fat rate prediction model is 0.985 4.In summary,the system pro-posed in the study can meet the functional requirements of daily body fat rate detection for athletes and achieve real-time detection.
body fatmonitoring systemPSO-ELM algorithmnetworkingbioelectrical impedance
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