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Multi-freedom-degree synchronous electromyographic control method based on deep learning

A technology of deep learning and degrees of freedom, applied in the field of myoelectric control, can solve the problems of lack of information of myoelectric signals, lack of interrelationship of degrees of freedom, etc., and achieve the effect of strong adaptability, good movement direction and movement trend

Pending Publication Date: 2018-08-28
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the existing multi-degree-of-freedom synchronous myoelectric control model that only learns information about one degree of freedom, lacks the interconnection between degrees of freedom, and extracts the characteristics of myoelectric signals accompanied by myoelectricity. The problem of lack of information in the signal

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  • Multi-freedom-degree synchronous electromyographic control method based on deep learning
  • Multi-freedom-degree synchronous electromyographic control method based on deep learning
  • Multi-freedom-degree synchronous electromyographic control method based on deep learning

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specific Embodiment approach 1

[0020] Specific implementation mode one: as figure 1 with 2 As shown, the multi-degree-of-freedom synchronous myoelectric control method based on deep learning described in this embodiment is specifically carried out in accordance with the following steps:

[0021] Step 1. Synchronous acquisition and storage of original EMG signals and corresponding motion information;

[0022] Step 2. Establish an initial multi-degree-of-freedom synchronous myoelectric control model based on a one-dimensional convolutional neural network;

[0023] Step 3. Input the original EMG signals of each group of experimental data collected in Step 1 into the initial multi-degree-of-freedom synchronous EMG control model established in Step 2, and correspondingly take the motion information of each group of experimental data as the output target, and obtain The final multi-degree-of-freedom synchronous myoelectric control model;

[0024] Step 4, collect the subject's original myoelectric signal, input...

specific Embodiment approach 2

[0029] Specific embodiment two: the difference between this embodiment and specific embodiment one is: the specific process of synchronous collection and storage of wrist motion information and electromyographic signals in step one is:

[0030] Select no less than 9 subjects to participate in the data collection. Each arm of the subject needs to collect no less than 3 sets of EMG data, and the two arms alternately collect data; that is, after collecting a set of data , the electrode is removed and placed on the other arm for data collection; the electrode will deviate from the last placement position by no more than 1cm during the repeated wearing process;

[0031] The movement information of the 3 degrees of freedom of the wrist is converted into the movement information of the horizontal movement, vertical movement and angular rotation of the cursor projected on the screen through the cross laser transmitter; when collecting data, the operator is only required to plan two at ...

specific Embodiment approach 3

[0035] Specific implementation mode three: as figure 2 shown. The difference between this embodiment and the specific embodiment two is: the specific process of step three is:

[0036] The original EMG signal collected in step 1 is intercepted by windowing and used as input. After N convolutional layers and pooling layers, the N+1th convolutional layer is the convolutional layer. The output length after convolution in the convolutional layer is is 1, and then through the further feature extraction of the fully connected layer, the information of multiple target degrees of freedom is finally output through the linear regression layer;

[0037] Among them, the value of N is greater than or equal to 1, and there is an activation function layer between each convolutional layer and the pooling layer, and an activation function layer between the N+1th convolutional layer and the fully connected layer.

[0038] The convolutional layer in this embodiment can change the expression f...

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Abstract

The invention discloses a multi-freedom-degree synchronous electromyographic control method based on deep learning, and belongs to the technical field of electromyographic control. According to the method, the problems that in an existing multi-freedom-degree synchronous electromyographic control study model, only information of one freedom degree is learned, the interrelation between freedom degrees is lacked, and information in electromyographic signals is missed when electromyographic signal features are extracted are solved. According to the method, wrist original electromyographic signalsand corresponding motion information of subjects are synchronously collected and stored, an initial multi-freedom-degree synchronous electromyographic control model is set up based on a one-dimensional convolution neural network, the collected original electromyographic signals are input into the initial multi-freedom-degree synchronous electromyographic control model, each set of wrist motion information is correspondingly taken as an output object, and therefore the trained multi-freedom-degree synchronous electromyographic control model is obtained; and the wrist original electromyographicsignals of the subjects are collected to be input into the final multi-freedom-degree synchronous electromyographic control model, the motion information of the wrists can be predicted, and the motion information can be applied to mechanical arm control.

Description

technical field [0001] The invention belongs to the technical field of myoelectric control, and in particular relates to a highly adaptable multi-degree-of-freedom synchronous myoelectric control method based on deep learning. Background technique [0002] The motion information decoding method is a prediction method that calculates the actual motion information by analyzing the electromyography signal. When the organism controls the movement of the muscles, the motor units in the muscles will respond to the information transmitted by the central nervous system and contract, and the accompanying electrical activity effects in this process can be collected by electrodes, which are called myoelectric signals ( Electromyography, EMG), to a certain extent, can reflect the user's control intention. By collecting and analyzing myoelectric signals, it is possible to predict the user's movements. [0003] The current relatively mature myoelectric control methods can be divided int...

Claims

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Application Information

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IPC IPC(8): B25J9/16
CPCB25J9/1605B25J9/163
Inventor 杨大鹏杨威顾义坤李佳铭刘宏
Owner HARBIN INST OF TECH
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