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sEMG gesture recognition method based on wavelet width learning system

A learning system, gesture recognition technology, applied in character and pattern recognition, input/output process of data processing, input/output of user/computer interaction, etc. The effect of recognition rate

Pending Publication Date: 2019-10-29
GUANGDONG UNIV OF TECH
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Problems solved by technology

However, these classic network models consume a lot of time during the training process

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  • sEMG gesture recognition method based on wavelet width learning system
  • sEMG gesture recognition method based on wavelet width learning system
  • sEMG gesture recognition method based on wavelet width learning system

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Embodiment Construction

[0043] The present invention will be further described in detail below in conjunction with the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0044] Such as Figure 1~3 Shown, a kind of sEMG gesture recognition method based on wavelet width learning system comprises the following steps:

[0045] Step 1: Define the number d of gesture types according to the corresponding recognition scene, and assign a sequence number to each gesture.

[0046] Step 2, use such as image 3 The shown electromyographic signal acquisition device acquires sEMG signal s.

[0047] Step 3: According to the frequency characteristics of the sEMG signal, filter the original sEMG signal for noise reduction, and use the Butterworth filter to remove the noise outside the 10Hz-500Hz frequency band:

[0048]

[0049] where N is the order of the filter, ω c filter for the cutoff frequency.

[0050] Step 4, using the moving window method...

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Abstract

The invention discloses an sEMG gesture recognition method based on a wavelet width learning system. The sEMG gesture recognition method comprises: 1, defining the number d of gesture action types according to a corresponding recognition scene; 2, acquiring an sEMG signal s by using electromyographic signal acquisition equipment; 3, filtering and denoising the sEMG original signal according to thefrequency characteristic of the sEMG signal, and removing noise outside the frequency band of 10Hz-500Hz by using a Butterworth filter; 4, detecting an active section in the sEMG signal by adopting amoving window method; step 5, carrying out feature extraction on the detected movable section. Compared with an algorithm based on a traditional deep learning network, the method has the advantages that model training and parameter determination can be completed more quickly, so that the working efficiency is improved; nodes can be dynamically expanded, so that the recognition rate of the systemis improved, and a model does not need to be completely rebuilt and trained.

Description

technical field [0001] The invention relates to the technical field of machine learning and signal classification, in particular to a sEMG gesture recognition method based on a wavelet width learning system. Background technique [0002] Gestures in body language play an important role in daily communication, such as referee actions on the football field and so on. Therefore, scholars have focused on how to enable computers and machines to efficiently and accurately recognize human gestures and execute corresponding programs. This will change the form of communication between humans and machines. [0003] At present, the recognition algorithms for gesture actions are mainly divided into the following categories: classification algorithms based on visual image recognition and gesture recognition algorithms based on surface electromyography (sEMG); Relatively high and the price of the device will be relatively expensive, so it is difficult to popularize, but the recognition ...

Claims

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Application Information

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IPC IPC(8): G06F3/01G06K9/00G06K9/62
CPCG06F3/015G06F3/017G06V40/28G06F18/23213G06F18/24
Inventor 林佳泰刘治章云
Owner GUANGDONG UNIV OF TECH
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