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Electromyographic signal identification method based on spherical mean value Lyapunov exponent and correlation dimension

A technology of myoelectric signal and recognition method, applied in the field of pattern recognition

Inactive Publication Date: 2013-03-13
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, due to the complexity of nonlinear problems and the wide range of application fields, there are still many topics to be studied

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  • Electromyographic signal identification method based on spherical mean value Lyapunov exponent and correlation dimension
  • Electromyographic signal identification method based on spherical mean value Lyapunov exponent and correlation dimension
  • Electromyographic signal identification method based on spherical mean value Lyapunov exponent and correlation dimension

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Embodiment Construction

[0055] The embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings: this embodiment is implemented on the premise of the technical solution of the present invention, and provides detailed implementation methods and specific operating procedures.

[0056] Such as figure 1 As shown, this embodiment includes the following steps:

[0057] Step 1: Obtain the sample data of the human upper limb EMG signal, specifically: firstly pick up the human upper limb EMG signal through the EMG signal acquisition instrument, and then use the signal denoising method based on wavelet energy spectrum entropy to denoise the EMG signal containing interference noise noise cancellation.

[0058] (1) Collect the EMG signals of the upper limbs of the human body. The subjects performed 60 groups of fist clenching, fist stretching, wrist flexion, and wrist extension respectively. The extensor carpi ulnaris and flexor carpi ulnaris of the uppe...

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Abstract

The invention provides an electromyographic signal identification method based on spherical mean value Lyapunov exponent and correlation dimension. The electromyographic signal identification method comprises the steps of first acquiring a corresponding surface electromyogram signal from a relevant muscle group, extracting the spherical mean value Lyapunov exponent and the correlation dimension of the electromyogram signal as characteristic vectors and finally inputting the spherical mean value Lyapunov exponent and the correlation dimension serving as the characteristic vectors into a binary-tree-structure classifier manufacture by a support vector machine to achieve recognition of an upper limb multi-locomotion mode of the electromyogram signal. The spherical mean value Lyapunov exponent and the correlation dimension are input into the support vector machine to finish the recognition of a hand action movement mode, a classification result of combined characteristics is better than that of a single characteristic, various classifier units adopting a transductive support vector machine (TSVM) have higher recognition rate that the classifier units adopting the traditional support vector machine (SVM).

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a method for pattern recognition of electromyographic signals, in particular to a method for recognizing multi-movement patterns of upper limbs based on electromyographic signals, which is applied to control a teleoperated robot. Background technique [0002] Surface electromyogram (sEMG) is the superposition of electrical signals generated by a large number of action units related to human muscle activity recruited on the muscle surface, and it is a nonlinear signal with good self-similarity. The study of EMG using nonlinear analysis methods, such as chaos and fractal theory, is a direction worthy of attention. [0003] In the field of teleoperation robot research, myoelectric signals can be used as the input interface of teleoperation, and the action commands obtained from the surface electromyography signals on the operator's limbs are used to control the remote mechanical dev...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/00
Inventor 张启忠席旭刚朱海港左静
Owner HANGZHOU DIANZI UNIV
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