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Ankle moment prediction method of recurrent cerebellar model based on surface electromyogram signal

An electromyographic signal and cerebellum model technology, applied in the field of human-computer interaction, can solve the problems of complex model structure and many physiological parameters, etc.

Active Publication Date: 2020-01-21
FUZHOU UNIV
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Problems solved by technology

However, the Hill model has a complex structure and many unknown physiological parameters

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  • Ankle moment prediction method of recurrent cerebellar model based on surface electromyogram signal
  • Ankle moment prediction method of recurrent cerebellar model based on surface electromyogram signal
  • Ankle moment prediction method of recurrent cerebellar model based on surface electromyogram signal

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[0034] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0035] The present invention provides a kind of recursive cerebellum neural network model foot ankle moment method based on surface electromyography signal, comprising the following steps:

[0036] (1) Use the surface electromyographic data and corresponding speed and position data of muscles such as gastrocnemius, tibialis anterior and peroneus longus in a certain time series as training data.

[0037] (2) Analyze and process the training data, perform preprocessing on the signal such as denoising and removing outliers, and perform data processing such as normalization, resampling, and deredundancy on the preprocessed data.

[0038] (3) The recurrent cerebellar neural network model is used to train the torque prediction on the processed data.

[0039] (4) Obtain the result of ankle moment prediction through the recursive cerebellum mode...

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Abstract

The invention relates to an ankle moment prediction method of a recurrent cerebellar model based on a surface electromyogram signal. The method uses an sEMG (Surface Electromyogram) data of the muscle(including but not limited to the gastrocnemius muscle, the tibialis anterior, the peroneus longus and the hallucis longus) relevant to the human body ankle joint and corresponding speed and positiondata are used; and a recurrent cerebellar model neural network is used for performing moment prediction on the human body ankle joint.

Description

technical field [0001] The invention relates to the field of human-computer interaction technology applied to the prediction of ankle torque, in particular to a method for predicting ankle torque based on a recursive cerebellar model of surface electromyographic signals. Background technique [0002] With the maturity and development of robot technology, the more frequent interaction between the human body and the robot makes the human-computer interaction technology become more and more important. Traditional human-computer interaction is generally carried out in the form of machines passively accepting human instructions. Obviously, this interaction method is difficult to apply to machine systems that are integrated with the human body, such as bionic prosthetics and rehabilitation robots, which aim to predict human muscle strength. Therefore, From passively receiving instructions to actively understanding user intentions is the future research direction of human-computer ...

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Application Information

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IPC IPC(8): A61B5/22A61B5/0488A61B5/00
CPCA61B5/224A61B5/7267A61B5/389
Inventor 姜海燕于守艳陈艳杜民
Owner FUZHOU UNIV
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