Infant neural development state analysis method and system

By collecting small infant movement data on the test bench and using machine learning methods for decision-making and fusion, the accuracy and convenience of neurodevelopmental status analysis in the prior art of small infants is solved, and the effect of predicting neurodevelopmental disorders is achieved earlier.

CN119679370AActive Publication Date: 2025-03-25GUANGZHOU KERUN MEDICAL TECHNOLOGY CO LTD +1
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Patent Information

Application Number
CN202411871397.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-18
Publication Date
2025-03-25
Estimated Expiration
2044-12-18

AI Technical Summary

Technical Problem

The prior art is difficult to accurately and conveniently analyze the neurodevelopmental status of young infants, especially in the early prediction of neurodevelopmental disorders.

Method used

By setting up cameras and audio modules on the test bench, the whole-body active movement and locally induced motion data of the baby are collected, the movement characteristics are extracted using the human posture estimation model and facial behavior analysis model, and decision-making fusion is carried out through machine learning methods such as logistic regression and support vector machine to achieve analysis and evaluation of neurodevelopmental status.

Benefits of technology

It improves the accuracy and convenience of neurodevelopmental status analysis in small infants, can predict neurological developmental disorders earlier, provide more effective rehabilitation intervention, and improve prognosis.

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Abstract

The invention discloses an infant neural development state analysis method and system, and belongs to the technical field of computer vision, and the method comprises the following steps: S1, data collection preparation; s2, collecting active motion data of the whole body; s3, collecting local induced motion data; and S4, multi-motion feature extraction and fusion decision making. On the basis of whole body spontaneous motion data acquisition and analysis, local motion data acquisition and analysis induced by visual and auditory sense organs are added, multi-modal behavioral features are obtained, and clinical classification accuracy is improved through later-stage fusion decision making of the modal features.
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