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Human motion intention recognition method and system for lower limb exoskeleton

A technology of human motion and recognition methods, applied in neural learning methods, character and pattern recognition, applications, etc., can solve the problems of difficult detection and recognition of human motion intentions, achieve convenient classification of human motion intentions, accurate classification effects, and reduce abnormal The effect of stationarity

Pending Publication Date: 2022-05-03
宁波工业互联网研究院有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Recognizing human motion intentions requires the fusion of multi-sensor information for decision-making, so although lower limb-assisted exoskeleton robots have obvious social and economic value, due to technical factors, current lower-limb-assisted exoskeleton robot products face difficulty in detecting and identifying human motion intentions, etc. Practical problems

Method used

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  • Human motion intention recognition method and system for lower limb exoskeleton
  • Human motion intention recognition method and system for lower limb exoskeleton
  • Human motion intention recognition method and system for lower limb exoskeleton

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Embodiment 1

[0050] In order to enable the lower extremity exoskeleton robot to better recognize the user's movement intention during operation, so as to provide assistance to the user more accurately, such as figure 1 As shown, the present invention proposes a human body movement intention recognition method for lower extremity exoskeleton, including steps:

[0051] S1: Obtain the signal set of lower limb myoelectric signal and inertial signal during human gait movement;

[0052] S2: Through the Butterworth filter and variational mode decomposition, the EMG signals in the signal concentration are sequentially denoised;

[0053] S3: Through the Butterworth filter and wavelet denoising, the inertial signal in the signal set is denoised in turn;

[0054] S4: Extract the time-frequency information of each signal in the noise-reduced signal set through continuous wavelet transform, and obtain the three-dimensional color map of the corresponding signal based on the time-frequency information; ...

Embodiment 2

[0080] In order to better understand the technology of the present invention, this embodiment illustrates the present invention in the form of a system structure, such as figure 2 As shown, a human motion intention recognition system for lower extremity exoskeleton, including:

[0081] The signal collector is used to obtain the signal set of the lower limb myoelectric signal and inertial signal during human gait movement;

[0082] The myoelectric signal processor is used to sequentially perform noise reduction processing on the myoelectric signals in the signal concentration through the Butterworth filter and the variational mode decomposition;

[0083] The inertial signal processor is used to sequentially perform noise reduction processing on the inertial signals in the signal set through the Butterworth filter and wavelet denoising;

[0084] The time-frequency information extractor is used to extract the time-frequency information of each signal in the signal set after noi...

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Abstract

The invention discloses a human body motion intention recognition method for a lower limb exoskeleton, and particularly relates to the technical field of human body exoskeleton control, and the method comprises the steps: obtaining a signal set of lower limb electromyographic signals and inertial signals during human body gait motion; noise reduction processing is conducted on the electromyographic signals in the signal set in sequence through a Butterworth filter and variational mode decomposition; sequentially carrying out noise reduction processing on the inertial signals in the signal set through a Butterworth filter and wavelet denoising; extracting time-frequency information of each signal in the signal set after noise reduction through continuous wavelet transform, and obtaining a three-dimensional color image of the corresponding signal based on the time-frequency information; performing off-line classification training on the double-flow convolutional neural network through the three-dimensional color image with the preset proportion; and carrying out human body motion intention identification verification on the three-dimensional color image of the remaining proportion through the trained double-flow convolutional neural network. According to the method, the human body motion intention is identified from two aspects of myoelectricity and inertia, so that a more accurate motion intention classification effect is obtained.

Description

technical field [0001] The invention relates to the technical field of human exoskeleton control, in particular to a method and system for recognizing human motion intentions for lower limb exoskeleton. Background technique [0002] The lower extremity exoskeleton robot is a wearable bionic mechatronics device. Through a variety of sensors to collect the user's movement intention and limb posture, computer programs to realize intelligent decision-making, and mechanical devices to provide power to assist and replace part or all of the lower limb functions of the human body. [0003] Recognizing human motion intentions requires the fusion of multi-sensor information for decision-making, so although lower limb-assisted exoskeleton robots have obvious social and economic value, due to technical factors, the current lower-limb-assisted exoskeleton robot products are difficult to detect and identify human motion intentions, etc. Practical problems. Contents of the invention ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/11A61B5/389A61B5/00G06V10/764G06V10/766G06V10/774G06V10/82G06N3/04G06N3/08G06K9/62B25J9/00
CPCA61B5/1118A61B5/112A61B5/389A61B5/7203A61B5/725A61B5/726A61B5/7264A61B5/7257A61B5/7267G06N3/08B25J9/0006G06N3/047G06N3/048G06N3/045G06F18/214G06F18/2415
Inventor 裘焱枫郏云涛邵良玉黄玉琳龙杜辉高熙强张克勤
Owner 宁波工业互联网研究院有限公司
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