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Sample training method, classification method, identification method, apparatus, medium and system

A sample training and sample technology, applied in the computer field, can solve the problem that the classification accuracy of the samples to be tested cannot be effectively guaranteed.

Active Publication Date: 2019-01-15
SHENZHEN INST OF ADVANCED TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a sample training method, classification method, low back pain symptom recognition method, computing device, computer readable storage medium and low back pain symptom recognition system, aiming to solve the problem that the existing technology cannot effectively guarantee the The problem of sample classification accuracy

Method used

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  • Sample training method, classification method, identification method, apparatus, medium and system
  • Sample training method, classification method, identification method, apparatus, medium and system
  • Sample training method, classification method, identification method, apparatus, medium and system

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Embodiment 1

[0033] figure 1 The implementation process of the sample training method provided by Embodiment 1 of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown, and the details are as follows:

[0034] In step S101, a first sample set composed of samples to be trained belonging to the first category is obtained, the first sample set includes: compared with reference samples belonging to the second category, the samples to be trained have no significant difference in feature change A second sample set composed of training samples, and, compared with the feature change of the reference sample, the sample to be trained has a significant difference between the feature change of the sample to be trained in the second sample set and the reference sample. The third set of samples formed by the samples.

[0035] In the embodiment of the present invention, the samples to be trained in the first sample...

Embodiment 2

[0041] figure 2 It shows the implementation flow of the classification method provided by Embodiment 2 of the present invention. The classification method is based on the first classifier and the second classifier implemented in Embodiment 1. The first classifier and the second classifier can be cascaded, Get a support vector machine (Support Vector Machine, SVM) classifier. For ease of description, only the parts related to the embodiments of the present invention are shown, and the details are as follows:

[0042] In step S201, a sample to be tested is obtained.

[0043] In step S202, the sample to be tested is processed to obtain the characteristics of the sample to be tested.

[0044] In step S203, the characteristics of the sample to be tested are input into the first classifier for the first judgment. If the obtained first judgment result indicates that the sample to be tested belongs to the first category, step S204 is executed; otherwise, step S205 is executed.

[...

Embodiment 3

[0051] image 3 It shows the implementation process of the low back pain symptom recognition method provided by the third embodiment of the present invention. For the convenience of explanation, only the parts related to the embodiment of the present invention are shown, and the details are as follows:

[0052] In step S301 , the EMG signals of local muscles of the waist of the subject are obtained.

[0053] In the embodiment of the present invention, electrode pads can be pasted on the surface of the waist muscles of the subject, and the electrode pads will record the bioelectric signals released during neuromuscular activity, that is, the above-mentioned local muscle electromyography signals of the waist.

[0054] In step S302, preprocessing is performed on the EMG signals of local muscles of the waist to obtain samples to be tested.

[0055] In the embodiment of the present invention, the preprocessing involves filtering, denoising and standardizing the EMG signals of loca...

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Abstract

The invention is applicable to the field of computer technology.A method for sample training,a classification method, a method for identifying symptoms of low back pain, a computing device, a COMPUTERREADABLE STORAGE MEDIUM AND a LOWER BACK PAIN SYMPTOM RECOGNITION SYSTEM are provided. The first classifier takes into account not only the generality of changes in sample characteristics in the second set of samples belonging to the first class compared to the reference sample characteristics of the second class, the relative scarcity of changes in sample characteristics in the third sample setbelonging to the first category compared to the reference sample characteristics belonging to the second category is also considered, so that it can be fast, classify more accurately, the second classifier focuses on the rare change of the sample features in the third sample set belonging to the first class compared to the reference sample features belonging to the second class, so that the secondclassifier can reclassify the first classifier correctively when the classification error occurs, thereby effectively ensuring the accuracy of the classification of the samples to be tested.

Description

technical field [0001] The invention belongs to the technical field of computers, and in particular relates to a sample training method, a classification method, a low back pain symptom recognition method, a computing device, a computer-readable storage medium, and a low back pain symptom recognition system. Background technique [0002] Due to the unbalanced distribution of information resources, for information belonging to the same category, some categories of information are obviously deficient in quantity but have significant differences with another reference category information, while some categories of information are obviously abundant in quantity but are different from the reference category information. There is no significant difference in the characteristics, so that the obviously scarce rare category information cannot be compared with the obviously abundant common category information in quantity. In the existing classification process, the rare category infor...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/00
CPCG06V40/10G06V40/15G06F18/24G06F18/214
Inventor 杜文静王磊李慧慧
Owner SHENZHEN INST OF ADVANCED TECH
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