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Muscle fatigue level feature extraction method and system using collaborative network

A collaborative network, muscle fatigue technology, applied in the fields of rehabilitation medicine and ergonomics, can solve problems such as poor robustness, and achieve the effect of solving poor robustness and good reliability

Active Publication Date: 2021-06-15
SUZHOU UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] For this reason, the technical problem to be solved by the present invention is to overcome the poor robustness of evaluating complex muscle fatigue in the prior art in view of time-frequency characteristics, thereby providing a Method and system for extracting muscle fatigue level features with good robustness of extracted features using collaborative network

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  • Muscle fatigue level feature extraction method and system using collaborative network
  • Muscle fatigue level feature extraction method and system using collaborative network
  • Muscle fatigue level feature extraction method and system using collaborative network

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Embodiment 1

[0028] Such as figure 1 As shown, this embodiment provides a method for extracting muscle fatigue level features using a collaborative network. Step S1: Divide multiple muscles on the body into multiple channels, collect myoelectric data corresponding to each channel, and collect the collected Preprocess the EMG data; Step S2: Calculate the Pearson correlation coefficient between different channels according to the preprocessed EMG data, and construct a collaborative network diagram between different channels according to the Pearson correlation coefficient; Step S3: Through the collaborative The network diagram analyzes the differences between different fatigue levels and extracts features. When extracting channel relationship features, the Pearson correlation coefficients between different channels are formed into a column matrix, and single-factor one-way analysis of variance is performed to obtain the analysis of variance between channels. As a result, by analyzing the pro...

Embodiment 2

[0059] Based on the same inventive concept, this embodiment provides a system for extracting muscle fatigue level features using a collaborative network. The principle of solving the problem is similar to the method for extracting muscle fatigue level features using a collaborative network, and the repetition will not be repeated. , the system specifically includes:

[0060] The collection preprocessing module is used to divide multiple muscles on the body into multiple channels, collect the corresponding myoelectric data of each channel, and preprocess the collected myoelectric data;

[0061] Building a collaborative network module, used to calculate the Pearson correlation coefficient between different channels according to the preprocessed myoelectric data, and construct a collaborative network diagram between different channels according to the Pearson correlation coefficient;

[0062] The analysis and extraction module is used to analyze the differences between different ...

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Abstract

The invention relates to a muscle fatigue level feature extraction method and system using a collaborative network, and the method comprises the following steps: dividing a plurality of muscles on a body into a plurality of channels, collecting the myoelectricity data corresponding to each channel, and carrying out the preprocessing of the collected myoelectricity data; calculating Pearson correlation coefficients among different channels, and constructing a collaborative network diagram among the different channels; analyzing differences among different fatigue levels and extracting features, including extracting channel relation features and extracting network structure features; and combining the extracted channel relation features with the network structure features to jointly form feature vectors of muscle fatigue levels. According to the method, the problem of poor feature robustness of muscle fatigue extraction can be effectively solved, the network diagram can intuitively show feature differences among different fatigue levels, and variance analysis and network parameters can provide quantitative results in numerical values, which has good reliability.

Description

technical field [0001] The invention relates to the technical fields of rehabilitation medicine and ergonomics, in particular to a method and system for extracting muscle fatigue level features using a collaborative network. Background technique [0002] For scenes that require long hours of work, such as driving a car, loading and unloading goods, etc., the muscles need to be in a state of contraction for a long time, and fatigue will appear in a continuous state. Bringing muscle damage, the accumulation of muscle fatigue will eventually lead to musculoskeletal damage. In order to avoid damage caused by muscle fatigue as much as possible, in the fields of rehabilitation medicine and ergonomics, it is necessary to formulate a corresponding rehabilitation plan or workload by detecting the fatigue state of human muscles. It has important application value in fields such as medicine and ergonomics. [0003] Currently, muscle fatigue status can be assessed by extracting surfac...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/389A61B5/296A61B5/257A61B5/00
CPCA61B5/4519A61B5/725
Inventor 郭浩公培浩李春光王翼鸣张虹淼李娟李伟达孙立宁
Owner SUZHOU UNIV
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