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Fatigue state causal network method based on multi-source data information

A fatigue state, causal network technology, applied in medical science, diagnosis, diagnostic recording/measurement, etc., to achieve high application value, improve analysis accuracy, and high precision

Active Publication Date: 2020-01-31
CAPITAL NORMAL UNIVERSITY
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Problems solved by technology

[0005] The purpose of the present invention is to provide a fatigue state causal network method based on multi-source data information, to overcome the defects of the above-mentioned machine learning method, and apply the Granger causality analysis method to analyze various physiological indicators that affect the fatigue state of the human body

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  • Fatigue state causal network method based on multi-source data information
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  • Fatigue state causal network method based on multi-source data information

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Embodiment Construction

[0030] The present invention will be clearly and completely described below in conjunction with the embodiments and accompanying drawings. Apparently, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0031] In the present invention, the descriptions involving "first", "second" and so on are only for the purpose of description, and should not be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features.

[0032] Please refer to figure 1 , figure 1 It is the realization flowchart of the method of the present invention.

[0033] Step S1, with K4b 2 The cardiopulmonary function tester collects the breathing data of 9 test subjects, and th...

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Abstract

The invention discloses a fatigue state causal network method based on multi-source data information. The fatigue state causal network method comprises the following steps that 1, breathing and heartrate data related to human fatigue state are collected; 2, according to physiological data, an extrapolation fitting method is used for removing outliers; 3, two of the physiological data are selectedat random, and whether a correlational relationship exists between the two data or not is tested; 4, for the two physiological data with the correlational relationship, a granger causality analysis method is used for testing whether a causal relationship exists between the two physiological data or not; 5, two sets of the physiological data are selected in a circular manner, and the granger causality analysis method in the step is utilized; and 6, after all physiological data are traversed, a causal relationship network between variables is established. The fatigue state causal network methodhas the advantages of high accuracy, small calculation amount and high compatibility and can be used for the causal analysis and detection of fatigue status of workers on vehicles such as automobiles, ships and aircraft, and the accuracy of causal analysis of fatigue status based on multi-source data fusion is improved, so that the application value is high.

Description

technical field [0001] The invention relates to a fatigue state causal network method based on multi-source data information, which can be used for causal analysis and detection of fatigue state of workers in vehicles, ships, airplanes, etc., and improves the accuracy of fatigue state causal analysis based on multi-source data information fusion . Background technique [0002] When working in a special environment, some physiological indicators of people will change, and these changes in physiological data may lead to the occurrence of certain physiological diseases. For example, if you work in a closed environment for a long time, you may be more prone to fatigue and depression. Fatigue status is an early warning of some physiological sub-health status, so the study of human fatigue status in special working environment will help to predict the health status of people, so as to reasonably improve their working conditions, which is of great significance. [0003] The machi...

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Application Information

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IPC IPC(8): A61B5/18A61B5/0205A61B5/0402
CPCA61B5/18A61B5/0205A61B5/7271A61B5/318
Inventor 任长娥袁超杜涛王岩
Owner CAPITAL NORMAL UNIVERSITY
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