Human body fatigue state prediction method and system based on fuzzy sensor

Abstract

The invention discloses a human body fatigue state prediction method and system based on a fuzzy sensor, and the method comprises: collecting breathing data through a cardiopulmonary function tester,collecting heart rate data through an electrocardio monitor, and collecting a fatigue state recognized by a testee through a questionnaire; normalizing the data to eliminate the influence of dimensions among the indexes; dividing the collected data into a training data set and a test data set, and designing a corresponding fuzzy sensor; implementing the fuzzy sensor, sending the training data setinto the fuzzy sensor for training, learning a group of weight values and storing the weight values; and applying the test data set to the fuzzy sensor to obtain a prediction result. The prediction result is compared with prediction results of other machine learning methods. The fuzzy theory is combined with a traditional linear sensor, the fuzzy sensor method is designed, the fuzzy concept similar to 'fatigue' in life can be classified through the method, and the application range of the classification method in machine learning is expanded.

Description

Technical field [0001] The invention relates to a method and system for predicting human fatigue state based on a fuzzy perceptron, belonging to the field of machine learning in artificial intelligence. Background technique [0002] Working in a special environment for a long time, certain physiological index values ​​of a person will be different from others, and changes in these physiological indexes may lead to the occurrence of certain diseases. For example, working in a dark or confined environment for a long time, people may be more prone to fatigue and depression. Human fatigue status is an early warning of certain physiological sub-health conditions. Therefore, studying people’s fatigue status under special working environments will help predict people’s health status so as to improve their working conditions reasonably, which is of great significance. [0003] At present, artificial intelligence technology is in the ascendant, and many machine learning methods have emerge...

AI Technical Summary

Problems solved by technology

However, as far as the neural network method is concerned, although it has strong self-learning ability and can better fit arbitrary functions, it is difficult to deal with some fuzzy information (such as fatigue, beautiful appearance)

In fuzzy mathematics theory, although there is a relatively complete fuzzy logic system for processing fuzzy information, the establishment of fuzzy logic systems requires prior knowledge, and its self-learning ability is poor.

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