A Dual-Data Factor Improved Body Fat Rate Prediction Method Under Impedance Information Deficiency
To solve the problem that intelligent wearable devices using bioelectrical impedance analysis can only measure local impedance of the human body and cannot accurately predict the overall body fat rate in the absence of impedance information,a body feature compensation factor and an improved parameter optimization aggregation factor based body fat rate prediction method are proposed.Firstly,based on the strong correlation between human body volume and impedance,measure the three circumference data and limb data that reflect human body shape,calculate a set of body feature compensation factors,and combine them with basic human body information and local impedance infor-mation to form a prediction model input matrix.Then,the parameter aggregation factor is introduced to improve the grey wolf algorithm,in or-der to enhance its search ability.Finally,using the improved grey wolf algorithm to optimize the traditional BP neural network model,a new body fat percentage prediction model was established and compared with other body fat percentage prediction models.The experiment shows that the average absolute error(MAE)of the two factor improved model is 0.659,the correlation coefficient R2 is 0.967,and the prediction ac-curacy AR is 90%,which is highly consistent with the measurement results of the eight electrode body fat measurement instrument.This study has certain theoretical and practical value for predicting the overall body fat rate using intelligent wearable devices.
local impedancewhole-body fathuman featurescompensation factoraggregation factorimproved grey wolf algorithmwearable device
万方数据
维普
