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A Myoelectric Feature Extraction Method Based on Memd Tensor Linear Laplacian Discrimination

A feature extraction and electromyography technology, applied in the field of electromyography signal processing, can solve problems such as limited projection direction and singularity of inter-class dispersion matrix, etc., achieve broad application prospects and meet the requirements of multi-mode recognition

Active Publication Date: 2020-08-11
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0005] The present invention aims at the traditional EMG feature extraction methods are often based on vectors, and use the Euclidean distance to calculate the dispersion matrix, so there are problems such as the singularity of the dispersion matrix between classes and the limited projection direction

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  • A Myoelectric Feature Extraction Method Based on Memd Tensor Linear Laplacian Discrimination
  • A Myoelectric Feature Extraction Method Based on Memd Tensor Linear Laplacian Discrimination
  • A Myoelectric Feature Extraction Method Based on Memd Tensor Linear Laplacian Discrimination

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Embodiment Construction

[0032] Below in conjunction with accompanying drawing describe in detail the present invention is based on the myoelectric feature extraction method of MEMD tensor linear Laplacian discriminant, figure 1 for the implementation flow chart.

[0033] Such as figure 1 , the implementation of the method of the present invention mainly includes six steps: (1) acquiring multi-channel myoelectric signal sample data during upper limb movements, including six steps such as upper limb wrist flexion, wrist extension, upper arm internal rotation, upper arm external rotation, fist clenching, and fist stretching. (2) MEMD method is used for filtering processing; (3) EMG signal is expressed as tensor by wavelet packet transform method, and tensor data with time, space, frequency and task are constructed; (4) TLLD is used method to calculate the optimal projection matrix of tensor data; (5) project the tensor data of EMG signals to the optimal projection matrix to obtain high-dimensional tens...

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Abstract

The invention relates to a method for extracting myoelectric features of MEMD tensor linear Laplacian discrimination. Traditional EMG feature extraction methods are often based on vectors and use Euclidean distance to calculate the dispersion matrix, so there are problems such as the singularity of the inter-class dispersion matrix and limited projection directions. The present invention is based on the data representation of the tensor structure, which can At the same time, multi-dimensional information such as time-frequency-space domain of the signal is considered. Firstly, the multi-variable empirical mode decomposition method is used to filter the multi-channel muscle signal, and then the wavelet packet transform is used to construct the fourth-order tensor data with time, space, frequency, and task, and then the tensor linear Laplacian discriminant method is used to find The optimal projection matrix is ​​used to obtain tensor high-dimensional features with a large degree of discrimination, then the high-dimensional tensor features are matrixed and dimensionally reduced, and finally the conventional classification method is used to identify the reduced-dimensional EMG features. This method has broad application prospects in the field of human-computer interaction such as rehabilitation robots.

Description

technical field [0001] The invention belongs to the field of electromyographic signal processing, and relates to a method for extracting features of electromyographic signals, in particular to a method for extracting features for human-computer interaction. Background technique [0002] The development of robot technology is strongly promoting the application of robots from industrial production to military, medical, service and other fields. In the future society, the communication between humans and robots and even the direct combination of each other's bodies will become more and more frequent, as the information channel connecting humans and robots Advanced human-computer interaction (HRI) technology will surely play a vital role in human life. The traditional human-computer interaction method based on program control shackles the robot's autonomous adaptability, and is difficult to apply to robot systems that need to be directly integrated with the human body, such as b...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06K9/00A61F2/72
CPCA61F2/72G06F2218/08G06F18/21322G06F18/21324G06F18/21326G06F18/22
Inventor 佘青山马鹏刚席旭刚蒋鹏
Owner HANGZHOU DIANZI UNIV
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