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Prosthetic hand control method based on MYO armlet

A control method and prosthetic hand technology, applied in prosthetics, medical science, etc., can solve the problems of data transmission being easily disturbed, data processing accuracy affecting real-time performance, etc., to achieve improved recognition accuracy, fast signal transmission speed, and low price Effect

Inactive Publication Date: 2016-09-21
SHANGHAI NORMAL UNIVERSITY
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Problems solved by technology

[0005] However, the sensor output in the myoelectric signal acquisition module is an analog signal. After data acquisition, the myoelectric signal acquisition module needs to be used for amplification, filtering, and analog-to-digital conversion. Transmission is susceptible to interference, and data processing accuracy affects real-time performance

Method used

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  • Prosthetic hand control method based on MYO armlet
  • Prosthetic hand control method based on MYO armlet
  • Prosthetic hand control method based on MYO armlet

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Embodiment

[0044] A prosthetic hand control method based on the MYO armband, which uses the MYO armband to collect the myoelectric signals of the arm muscles in real time, read the myoelectric signals and extract their eigenvalues, and use the eigenvalues ​​and the trained neural network model online Identify the hand movement pattern, convert the movement pattern into the corresponding motor movement command, and drive the prosthetic hand to make corresponding movements. This process is online recognition;

[0045] The neural network model training method includes: performing human hand movements, using the MYO armband to collect myoelectric signals of arm muscles, reading the myoelectric signals and extracting their eigenvalues, and training hand movements according to samples of eigenvalues. Neural network model, this process is an offline process before online recognition.

[0046] The offline training process of the neural network model and the storage of the model parameters of the...

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Abstract

The invention relates to a prosthetic hand control method based on an MYO armlet. An electromyographic signal of arm muscles is acquired in real time through the MYO armlet and read, the characteristic value of the electromyographic signal is extracted, hand motion mode is recognized online by means of the characteristic value and a well trained neural network model, and the motion mode is converted into a corresponding motor movement instruction which drives a prosthetic hand to make a corresponding motion; a method for training the neural network model comprises the steps of executing the hand motion, acquiring the electromyographic signal of arm muscles through the MYO armlet, reading the electromyographic signal, extracting the characteristic value of the electromyographic signal, and training the hand motion neural network model according to a sample of the characteristic value. Compared with the prior art, the method has the advantages of being convenient to use, low in cost, high in cost performance, wide in application range and the like.

Description

technical field [0001] The invention belongs to the technical field intersecting computer and rehabilitation engineering, and in particular relates to a method for controlling a prosthetic hand based on an MYO armband. Background technique [0002] The current data survey on the disabled shows that the number of physically disabled patients in China is as high as 24.12 million, accounting for about 1.83% of the total population, including 2.26 million amputee patients. It is conservatively estimated that more than 250,000 patients need prosthetic hands. Therefore, there is a huge market for prosthetic hands. At present, the domestically developed myoelectric prosthetic hands are still mainly in single-action mode, and the high-end myoelectric prosthetic hands with multi-action mode mainly rely on imports. [0003] Myoelectric signals are a reliable signal source for prosthetic hand control and are widely used in myoelectric prosthetic hands. The quality of myoelectric senso...

Claims

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Application Information

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IPC IPC(8): A61F2/72
CPCA61F2/72A61F2002/704
Inventor 李传江王朋张崇明
Owner SHANGHAI NORMAL UNIVERSITY
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