Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Fatigue prediction method based on Bayesian optimization XGBoost algorithm

A prediction method and optimization technology, which is applied in calculation, health index calculation, computer parts, etc., can solve problems such as the need to improve the accuracy of prediction

Inactive Publication Date: 2020-10-23
BEIHANG UNIV
View PDF5 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the prediction accuracy still needs to be improved

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Fatigue prediction method based on Bayesian optimization XGBoost algorithm
  • Fatigue prediction method based on Bayesian optimization XGBoost algorithm
  • Fatigue prediction method based on Bayesian optimization XGBoost algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0025] The present invention is a fatigue prediction method based on Bayesian optimization XGBoost algorithm, see figure 1 and figure 2 As shown, the steps are as follows:

[0026] Step 1. Use the signal acquisition instrument to obtain and store the physiological signal data of the tester in the state of exercise fatigue

[0027] Step 2. Eliminate abnormal data based on RANSAC algorithm

[0028] Step 3. Use the SMOTE oversampling algorithm to resample a small number of samples to solve the class imbalance problem

[0029] Step 4: Use the XGBoost model for fatigue prediction, and input the processed sample data into the XGBoost model for classification

[0030] Step 5. Use the Bayesian optimization algorithm to optimize the XGBoost model

[0031] Among them, the method of step one is as follows:

[0032] The data that the signal collector needs to collect includes the user's real-time blood pressure, blood oxygen saturation, heart rate, and body temperature. At the same...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06K9/62G16H50/30A61B5/0205
CPCG16H50/30A61B5/0205G06F18/24155G06F18/24323
Inventor 赵琦马裕静陈立江刘秉昊刘通尤玉虎
Owner BEIHANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com