Fatigue prediction method based on Bayesian optimization XGBoost algorithm

Abstract

The invention provides a fatigue prediction method based on a Bayesian optimization XGBoost algorithm. The method comprises the following steps: 1, obtaining physiological signal data of a tester in amovement fatigue state through a signal collection instrument, and storing the physiological signal data; 2, eliminating abnormal data based on an RANSAC algorithm; 3, performing minority sample resampling by using an SMOTE oversampling algorithm to solve the class imbalance problem; 4, performing fatigue prediction by using the XGBoost model, and inputting the processed sample data into the XGBoost model for classification; and 5, optimizing the XGBoost model by using a Bayesian optimization algorithm. Through the above steps, a fatigue prediction process is realized, and the intelligence and accuracy of exercise fatigue recognition are improved.

Description

technical field [0001] The invention provides a fatigue prediction method, in particular to a fatigue prediction method based on Bayesian optimization XGBoost algorithm, which belongs to the field of intelligent health management. Background technique [0002] As the basic module of the intelligent health management system, physical sign data monitoring is an indispensable core part. For a long time, it has always been a key topic of research by experts and scholars in various fields. With the continuous progress and development of society, coupled with the accumulation of personal health information for a long time, the intelligent health management system is gradually becoming more and more perfect. breakthrough progress. [0003] The entry point for the research on the problem of fatigue prediction at home and abroad can be roughly divided into the following four methods: evaluative measurement method, physiological response test method, physiological parameter test meth...

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