一种阻抗信息缺失下双数据因子改进的体脂率预测方法

A Dual-Data Factor Improved Body Fat Rate Prediction Method Under Impedance Information Deficiency

为解决生物电阻抗分析法智能可穿戴设备只能测量人体局部电阻抗,在阻抗信息缺失时无法准确预测全身体脂率的问题,提出一种体特征补偿因子和改进参数优化聚合因子的体脂率预测方法。首先,根据人体体积与阻抗的强相关性,测量反映人体体型的三围数据和肢体数据,计算出一组体特征补偿因子,将其与人体基本信息和局部阻抗信息结合,组成预测模型输入矩阵。然后,引入参数聚合因子对灰狼算法进行改进,以提升算法搜索能力。最后,利用改进的灰狼算法优化传统BP神经网络模型,建立一种新的体脂率预测模型,并与其他体脂率预测模型进行比较。实验表明,双因子改进模型平均绝对误差(MAE)为0.659、相关系数R2为0.967、预测准确率AR为90%,与八电极体脂测量仪测量结果高度一致性。该研究对于使用智能可穿戴设备进行全身体脂率的预测具有一定的理论和实践价值。

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.