A multi-granularity stratification method for the elderly physical fitness

A multi-granularity stratification method for the elderly physical fitness was proposed to overcome the tedious test items and time-consuming problem. Firstly, eleven key features were selected by three methods-multiple regression, random forest and BP neural network. Secondly, the individuals were divided into three different walking speed grades according to their speed characteristics. Finally, two logistic regression models were trained with the adjacent level of speed grades, and the physical fitness problem was stratified into seven levels based on the ensemble classification method. The experiment results show the basic features and disability score of people in different physical fitness levels have obvious statistical difference. The method can be utilized to assess the elderly physical fitness, and be beneficial for formulating the procedures and guidance.