Machine learning-based method for evaluating and predicting ASD

A machine learning and histogram technology, applied in the field of image processing, can solve the problems of no further prediction, no regional distinction, large region, etc.

Active Publication Date: 2015-11-18
SYSU CMU SHUNDE INT JOINT RES INST
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

The disadvantage of the AOI method is that the areas it divides are relatively large, and these areas are not further distingu

Method used

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  • Machine learning-based method for evaluating and predicting ASD
  • Machine learning-based method for evaluating and predicting ASD
  • Machine learning-based method for evaluating and predicting ASD

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Experimental program
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Embodiment

[0045] The program as a whole can be divided into four steps, which are collecting data, extracting features, training classifiers and making predictions.

[0046] like figure 1 Shown is a flow chart of the method for evaluating and predicting autism in the present invention, including the following steps:

[0047] Step 1, collect the eye movement data of the eyes of individuals with autism and normal individuals to scan the face.

[0048] Specifically, an eye tracker is used to display a face picture on the screen of the eye tracker, and the eye tracker will record the data of the face position that the participating experimenters are looking at, and obtain a coordinate corresponding to the image.

[0049] In this embodiment, TobiiT60 eye tracker is used, its sampling rate is 60Hz, and the screen resolution is 1024×768 pixels. A group of 700×500 pixel face pictures were displayed on the screen of the eye tracker, and the eye tracker automatically recorded the coordinates of t...

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Abstract

The present invention discloses a machine learning-based method for evaluating and predicting ASD, comprising the following steps: S1. data collection: using an eye tracker to separately collect eye movement data that an eye ball scans a human face when persons participating in a test watch a human face image, wherein the persons participating in the test comprise individuals who have ASD and normal individuals; S2. feature extraction: dividing the human face image into different areas according to collected eye movement coordinate data, extracting a feature from the original data collected by the eye tracker, and making a mark; S3. classifier training: training a classifier by using a marked feature, to obtain a classifier model for predicting ASD; and S4. prediction: testing on a test subject by using the classifier model for predicting ASD acquired in step S3, to evaluate and predict severity of autism of the test subject. The present invention may be considered as a supplementary method of ASD evaluation, so that ASD evaluation and prediction in an early stage is more accurate and convenient.

Description

technical field [0001] The present invention relates to the field of image processing, and more specifically, relates to a method for evaluating and predicting autism based on eye movement patterns based on machine learning. It is a method based on machine learning technology, which is based on the saccade pattern of the subject when observing a face picture. To evaluate methods for predicting autism spectrum disorder. Background technique [0002] Social attention to autism spectrum disorder (autismspectrumdisorder, ASD) has risen sharply in the past few years. In the United States, 1 in 68 people will suffer from autism. Although the existing ASD assessment methods are very effective, they are time-consuming and labor-intensive; and most of the diagnostic methods mainly evaluate the three aspects of language communication impairment, social communication impairment, and repetitive stereotyped behavior. Now the most widely used measurement methods include the Autism Diagno...

Claims

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Application Information

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IPC IPC(8): G06F19/00G06K9/62
Inventor 李明刘文博易莉蔡丹蔚
Owner SYSU CMU SHUNDE INT JOINT RES INST
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