A Wearable Body Fat Distribution Feature Low Error Detection Algorithm Based on Kernel Density Estimation

In order to improve the accuracy of wearable body fat distribution feature detection, a wearable body fat distribution feature detection algorithm based on kernel density estimation is designed. This paper collects human body data and classifies the data features. In this paper, the kernel density estimation method is used to extract the data features of body fat distribution. It judges the similarity of the data and realizes the wearable body fat distribution feature detection based on the kernel density estimation. The experimental results show that the proposed detection algorithm can accurately detect the wearable body fat distribution characteristics and meet the design requirements of the method.