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Random vector functional-link network technique-based human mechanomyography (MMG) signal prediction method

A technology that connects networks and random vectors. It is applied in the field of signal processing and can solve problems such as low accuracy of prediction algorithms, incomplete denoising of muscle tone signals, and high complexity.

Active Publication Date: 2020-05-08
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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Problems solved by technology

[0014] The purpose of the present invention is to provide a human muscle sound signal prediction method based on random vector functional connection network technology to solve the problems of incomplete muscle sound signal denoising and low prediction algorithm accuracy and high complexity in the prior art. question

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  • Random vector functional-link network technique-based human mechanomyography (MMG) signal prediction method
  • Random vector functional-link network technique-based human mechanomyography (MMG) signal prediction method
  • Random vector functional-link network technique-based human mechanomyography (MMG) signal prediction method

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[0091] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and accompanying drawings are used for a detailed description, as follows:

[0092] Such as figure 1 As shown, a kind of human muscle sound signal prediction method based on random vector functional connectivity network technology described in the present invention comprises the following steps:

[0093] The first step is the acquisition of human muscle tone signals.

[0094] Collect and save the acceleration signal of the wearable muscle sound signal sensor in the human elbow muscle, and obtain the acceleration information of the human elbow muscle in the three directions of x-axis, y-axis and z-axis from the 1st moment to the 3000th moment, and form 3 groups of lengths Both are muscle sound cent signals of 3000, recorded as:

[0095]

[0096] The second step is the integration of muscle sound signals: t...

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Abstract

The invention relates to a random vector functional-link network technique-based human mechanomyography (MMG) signal prediction method, and compared with the prior art, the method eliminates the defects that noise-removal processing of an MMG signal is not thorough, a prediction algorithm has low precision and high complexity, and the like. The method comprises the steps of acquisition of a humanMMG signal; integration of the MMG signal; noise-removal processing of the integrated MMG signal; building and training of a random vector functional-link network model; and prediction of the integrated MMG signal. According to the method, the MMG signal of a human elbow joint can be collected through a wearable MMG signal sensor, and the MMG signal is rapidly acquired and accurately predicted.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a human muscle sound signal prediction method based on random vector functional connection network technology. Background technique [0002] Human muscle sound signal (Mechanomyography, MMG), as a biological signal used to study muscle function, is an acceleration signal generated by muscle contraction. When the human muscle contracts, the cross-section of the muscle fiber changes, producing horizontal low-frequency vibrations. The sound is transmitted to the surface of the skin, and the strength of the signal is directly proportional to the degree of muscle tension. Myotone signal is a time-series signal with nonlinear and non-stationary characteristics, in which active muscle fiber twitches can be accumulated linearly or nonlinearly. Muscle sound signals have been widely used in the research of muscle fatigue, muscle strength detection, human rehabilitation engineeri...

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Application Information

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IPC IPC(8): A61B7/00A61B5/11A61B5/00
CPCA61B7/00A61B5/1118A61B5/6801A61B5/7203A61B5/7225A61B5/7264
Inventor 高理富陆伟李泽彬谢陈磊
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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