Electromyographic signal gesture recognition method based on double-flow network

A technology of myoelectric signal and gesture recognition, which is applied in the field of human-computer interaction and artificial intelligence, can solve the problems of neglecting time correlation, etc., and achieve the effect of increasing recognition accuracy, improving recognition accuracy, and reasonable design

Pending Publication Date: 2020-01-07
ZHEJIANG UNIV OF TECH
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Although good results have been achieved, sEMG is a kind o

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  • Electromyographic signal gesture recognition method based on double-flow network
  • Electromyographic signal gesture recognition method based on double-flow network
  • Electromyographic signal gesture recognition method based on double-flow network

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[0031] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0032] A kind of myoelectric signal gesture recognition method based on double stream network, comprises the steps:

[0033] Step 1. Use figure 1 A set of non-invasive wearable EMG collection equipment is shown to collect 8 healthy volunteers as figure 2 The sEMG data of a total of five gestures are shown, and a total of 240 sEMG samples were obtained. Each sample contains 195 frames of EMG data. Each frame of EMG consists of 300 milliseconds of sEMG, and the acquisition device has 16 electrode channels, so the dimension of EMG is 300×16.

[0034] Step 2. According to image 3 As shown, a two-stream network model is established, which is mainly composed of a multi-layer CNN and a multi-layer LSTM. There are five layers in the CNN part of the model: the first two layers are convolutional layers, which contain 64 convolution kernels (5×5, ...

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Abstract

The invention discloses an electromyographic signal gesture recognition method based on a double-flow network. The electromyographic signal gesture recognition method comprises the following steps: 1)collecting electromyographic signals of various gestures of multiple persons, wherein each gesture action of a subject lasts for 12 seconds by wearing a 16-channel acquisition device, steady-state data of 10 seconds are extracted, data preprocessing is carried out, a 300ms time window is selected, and the size of each frame of electromyogram is 300*16, so that a training set is constructed. 2) constructing a double-flow network model, wherein the model is mainly composed of three parts, the first part is a multi-layer CNN and is responsible for extracting spatial features; the second part isa multi-layer LSTM and is responsible for learning time characteristics; and the last part is a feature merging layer which is responsible for feature fusion. 3) training the double-flow network model, and performing gradient descent optimization by adopting an Adam optimizer until convergence, and 4) performing gesture recognition on the sEMG of the arm by using the trained double-flow network model.

Description

technical field [0001] The invention relates to the fields of human-computer interaction and artificial intelligence, in particular to a dual-stream network-based myoelectric signal gesture recognition method, which can be applied in industrial control and medical prosthesis. Background technique [0002] By constructing a deep learning model to classify the surface electromyography signal (sEMG), the electromyography signal is converted into instructions for conveying the user's movement intention, and then transmitted to the machine to form a complete electromyography control system. Gesture recognition based on surface EMG signals is the core of EMG control systems. In the application scenario, sEMG is susceptible to interference from the external environment, such as electrode offset, changes in muscle contraction force, and changes in muscle contraction force. These factors will affect the accuracy of the recognition model. In the application fields of sEMG, such as in...

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IPC IPC(8): G06F3/01G06N3/04G06N3/08
CPCG06F3/015G06F3/017G06N3/08G06N3/044G06N3/045
Inventor 杜怡辰张敏霞仝润泽俞辉
Owner ZHEJIANG UNIV OF TECH
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