Baby abnormal behavior detection method based on condition generative adversarial network and SVM

A conditional generation and detection method technology, applied in the field of video image processing and deep learning, can solve the problems of infant interference, difficulty in obtaining ideal results, and inaccurate prediction results.

Inactive Publication Date: 2019-04-16
JILIN UNIV
View PDF17 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The first method is to use a specific video recording method for infants, and use the whole body motion quality evaluation criteria to judge whether the behavior is abnormal. This method mainly relies on observation and has a certain degree of subjectivity.
The second method is to wear a sensor device for the baby to observe the parameters, but this wearable method itself will cause some interference to the baby's movement, resulting in inaccurate prediction results
The third method is to use the computer to extra

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Baby abnormal behavior detection method based on condition generative adversarial network and SVM
  • Baby abnormal behavior detection method based on condition generative adversarial network and SVM
  • Baby abnormal behavior detection method based on condition generative adversarial network and SVM

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0066] The implementation process of the present invention will be further described below in conjunction with the accompanying drawings.

[0067] A baby abnormal behavior detection method based on conditional generative confrontation network and SVM, the overall implementation process, such as figure 1 As shown, the method includes the following steps:

[0068] 1. Obtain baby video and perform unified preprocessing.

[0069] 2. Cut out 15s of the baby video in step 1, and name it uniformly, and name the images converted into frames uniformly.

[0070] 3. Tracking of the baby's movement trajectory: For the frame image obtained in step 2, use the conditional generative confrontation network CGAN to track the baby's limbs and the overall movement trajectory of the whole body, the flow chart is as follows figure 2 As shown, it specifically includes the following steps:

[0071] 3.1 Construct the training sample library required for target tracking, mark the baby's left hand, ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a baby abnormal behavior detection method based on a condition generative adversarial network and an SVM, and belongs to the technical field of video image processing and deeplearning. Whether baby behaviors are abnormal or not is judged by analyzing baby motion trajectories in videos, firstly, baby videos are obtained, the baby videos are cut with reasonable length and converted into frame images, and the four limbs and the whole body are marked so that a sample library can be built; then, the condition generative adversarial network is used for performing target tracking on the whole body and the four limbs of the baby; then, wavelet approximation waveform and wavelet power spectrum calculation is performed on the obtained target motion trajectory, obtained characteristics are classified through the SVM, and comprehensive judging is performed; motion trajectory detection is performed on information of the whole body and the four limbs of the baby, informationis more comprehensive compared with single limb detection, wavelet region and power spectrum combined training is adopted, the detection precision is improved, whether baby behaviors are abnormal ornot is detected, intervention is performed as soon as possible, and the method is of a great significance for preventing diseases such as baby brain paralysis.

Description

technical field [0001] The invention belongs to the technical field of video image processing and deep learning, and in particular relates to a method for detecting abnormal behavior of infants based on conditional generative confrontation network and SVM. Background technique [0002] Abnormal behaviors of infants mainly refer to that within five months of birth, there is no small and medium-speed movement in all directions with variable acceleration throughout the whole body, and other forms of movement suitable for age (such as midline movement of limbs, hand-knee touch) , visual search, fingers grabbing clothes, etc.) and the overall movement fluency is not good. Abnormal behaviors of infants correspond to brain damage, which may lead to cerebral palsy in severe cases. Since cerebral palsy is usually diagnosed after a child is one to two years old, it is very important to study the detection of abnormal behaviors in early infants and to intervene in time. practical sign...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): A61B5/11A61B5/00
CPCA61B5/11A61B5/1114A61B5/1127A61B5/7264
Inventor 王世刚戴晓辉赵岩韦健
Owner JILIN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products