Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A method and system for processing outliers in electromyographic data

A processing method and technology of outliers, applied in character and pattern recognition, instruments, cognitive medicine/anatomical patterns, etc., can solve outlier non-noise group misidentification, outlier sample group identification, Problems such as the impact of point model generalization ability, to achieve the effect of evaluating the degree of correlation

Active Publication Date: 2021-02-26
INST OF COMPUTING TECH CHINESE ACAD OF SCI
View PDF1 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem of the influence of outliers in the data set on the generalization ability of the model in the process of establishing the abnormal muscle recognition model, the present invention mainly solves the problem that the existing outlier point recognition method misidentifies the outliers and non-noise groups caused by insufficient samples in the data set The problem that the group is mistakenly identified as an outlier and deleted, and it also solves the problem that the existing method cannot identify all the outlier sample groups with noise patterns

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
  • A method and system for processing outliers in electromyographic data
  • A method and system for processing outliers in electromyographic data
  • A method and system for processing outliers in electromyographic data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0037] The core objective of the present invention is to cluster the boundary area samples into small groups according to the category based on the neighborhood rough set, and calculate its clustering level according to the neighborhood distribution characteristics of each boundary area sample, so as to obtain each small group The average clustering grade of . After marking groups whose clustering level is lower than the threshold as outliers, in order to prevent high-density outlier non-noise samples from being misidentified as outliers due to insufficient sample size, we will have The groups with lower clustering level are removed from the outlier group set, and the samples in the outlier group are marked as outlier samples, so as to identify the outlier samples.

[0038] Wherein, the data input unit may receive the myoelectric feature data or original myoelectric data obtained by the data acquisition device and manually marked, so as to be aggregated into an original data s...

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 present invention relates to a method and system for processing outliers in electromyographic data, comprising: obtaining a data set of data samples, and dividing the data set into a first-type positive domain, a first-type boundary area, a second-type positive domain, and a second-type positive domain The second type of boundary area; clustering the data samples in the first type of boundary area and the second type of boundary area, and according to the clustering results, respectively dividing the first type of boundary area and the second type of boundary area into multiple groups; According to the distance from the data samples in the first type of boundary area to the first type of positive domain and the distance from the data samples in the second type of boundary area to the second type of positive domain, determine the distance between each group in the first type of boundary area and the first type of positive domain. The average distance of the domain and the average distance from each group of the second type of boundary area to the second type of positive domain, the group whose average distance is greater than the distance threshold is marked as an outlier point, and the set of outlier points is used as an outlier set; the data Outliers whose sample size is greater than the enclave threshold are marked as enclave groups, and the enclave groups in the outlier set are deleted to obtain the outlier processing results.

Description

[0001] Technical field [0002] The invention belongs to the field of machine learning, and in particular relates to a method and system for processing outliers in electromyographic data, which is applicable to the scene of using electromyographic signals to detect abnormal muscles. Background technique [0003] When using EMG signals for abnormal muscle detection, a large number of EMG samples with labels (that is, abnormal muscles and normal muscles) are often required, and an abnormal muscle detection model is trained based on this data set to achieve the purpose of identifying abnormal muscles . It is hoped that the abnormal muscle recognition model can achieve a good generalization effect on the real data set in the future, but the generalization ability of the model is closely related to the quality of the data set. Due to various external influences in the process of EMG signal acquisition, such as motion interference, electromagnetic interference, power frequency nois...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06V2201/03G06F18/23G06F18/24133
Inventor 王念崔莉赵泽
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com