A method for extract characteristics of electromyography signal

A technology of myoelectric signal and feature extraction, applied in neural learning methods, biological feature recognition, instruments, etc., can solve the problems of insufficient frequency domain feature extraction and large change of time domain features, so as to reduce input data bandwidth and reduce redundancy. In addition, solve the effect of large changes in time domain characteristics

Active Publication Date: 2019-02-05
HOHAI UNIV CHANGZHOU
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Problems solved by technology

[0004] In order to solve the above problems, the present invention proposes a method for feature extraction of electromyographic signals to realize feature extraction of electromyographic signals and solve the problems of large changes in time domain features and insufficient frequency domain feature extraction faced by existing electromyographic signal extraction methods

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  • A method for extract characteristics of electromyography signal
  • A method for extract characteristics of electromyography signal
  • A method for extract characteristics of electromyography signal

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[0030] The technical solutions of the present invention will be further elaborated below according to the drawings and in conjunction with the embodiments.

[0031] The myoelectric signal data used in this embodiment is the myoelectric signal data obtained by four people doing ten different gestures. The different gestures of each person represent a category, and each gesture is an 8-dimensional data. The data has 10000 sample points. A feature extraction method for electromyographic signals, constructing a stacked autoencoder network architecture, that is, stacking multiple autoencoders, each autoencoder is a three-layer neural network, setting the parameters of each layer of neural network and using random gradients The descent method (Stochastic gradient descent, SGD) is used to train and update the weights; a restricted Boltzmann machine (RBM) with only the visible layer and the hidden layer is established, and the gradient descent method is used to train and update the we...

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Abstract

The invention discloses a myoelectric signal characteristic extraction method. Firstly, the myoelectric signal is changed into Fourier coefficients by Fourier transformation, and the Fourier coefficients of a preset frequency band are selected and normalized. A stack automatic encode is constructed, and network parameter of each layer are set for training and updating parameters; A stacked restricted Boltzmann machine is constructed, and the parameters of each layer network are set for training and updating. At that completion of the pre-trainning of the automatic encoder and the limit Boltzmann machine, The parameters of the two parts of pre-training are connected together, that is, the output of the automatic encoder is the input of the limited Boltzmann machine, which forms a through-network to realize the feature extraction of EMG signal, and solves the problems that the time-domain feature changes greatly and the frequency-domain feature extraction is inadequate when the existingEMG signal extraction methods are used.

Description

technical field [0001] The invention relates to the technical field of signal feature extraction, in particular to a method for extracting features of electromyographic signals. Background technique [0002] EMG signals can reflect neuromuscular activity to a certain extent, and have important practical value in clinical medicine, ergonomics, rehabilitation medicine, and sports science. Feature extraction can discover more meaningful latent variables, help to understand EMG data more deeply, reduce data storage and input data bandwidth, and reduce redundancy. [0003] The traditional EMG signal processing method regards the EMG signal as a random signal whose mean value is zero and whose variance changes with the signal intensity. The extraction of time-domain features is relatively simple, so the time-domain analysis method has been widely used in the field of EMG signal application. However, although the time-domain characteristics are easy to extract, a large number of ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/08
CPCG06N3/084G06V40/10G06F18/214
Inventor 徐宁刘妍妍倪亚南潘安顺刘小峰
Owner HOHAI UNIV CHANGZHOU
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