Automatic estrus ability identification method based on individual eye movement characteristics

Abstract

The invention discloses an automatic estrus ability identification method based on individual eye movement characteristics. The method comprises the following steps: collecting test eye movement dataof a testee based on a preset visual material; acquiring estrus response data of the testee; extracting eye movement characteristics of the testee according to the test eye movement data, wherein theeye movement characteristics comprise global eye movement characteristics and local eye movement characteristics; inputting the eye movement features and the estrus response data into a machine learning model used for evaluating estrus ability so as to train a prediction model; and collecting test eye movement data based on a preset visual material, inputting the test eye movement data into the prediction model obtained after training, and determining the common estrus ability level of the testee corresponding to the test eye movement data according to the output data of the prediction model.The technical scheme of the invention aims to provide a non-invasive, more efficient and convenient common-estrus capability identification method.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to an automatic empathy ability recognition method based on individual eye movement characteristics. Background technique [0002] In psychology, empathy often refers to the behavior of individuals perceiving and understanding the emotions of other individuals and responding correctly, which has always been a hot issue in current psychological research. The core meaning of empathy is "understanding and feeling for someone". [0003] Due to the important role of empathy in the process of human social cooperation, accurate measurement of individual empathy ability is of great value in many fields. For example, to understand the structure and function of empathy, it is necessary to have a reliable measure of empathy, which is the basis of scientific research; measuring individual empathy can help us understand individual cooperation, prosocial behavior, moral It has extensiv...

AI Technical Summary

Problems solved by technology

In addition, errors caused by memory, fatigue, individual response bias, repeated measurement, etc. are also difficult problems that cannot be solved by the above methods

Furthermore, in the process of manual measurement, one measurement can only be performed on one subject, and it is impossible to measure multiple subjects at the same time, and the measurement efficiency is low

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