A gesture recognition method based on a surface electromyography signal

A technology of myoelectric signal and gesture recognition, which is applied in neural learning methods, character and pattern recognition, manipulators, etc., to achieve the effects of improving experience, realizing real-time control, and simplifying calculations

Inactive Publication Date: 2019-01-15
SHENYANG SIASUN ROBOT & AUTOMATION
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Problems solved by technology

However, for the above three fields, how to choose the optimal feature extraction method is still a huge problem

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  • A gesture recognition method based on a surface electromyography signal
  • A gesture recognition method based on a surface electromyography signal
  • A gesture recognition method based on a surface electromyography signal

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[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is an embodiment of a part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0027] The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention and the above drawings are used to distinguish similar objects and not necessarily Describe a specific order or sequence. It is to be understood that the terms so used are interchangeable under appropriate cir...

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Abstract

The invention discloses a gesture recognition method based on a surface electromyography signal, which utilizes a sensor to collect the surface electromyography signal data. Seven feature extraction combinations were used to extract features from surface electromyography data. Three different classifiers are used to classify and compare the data after feature extraction. The average accuracy of the classification results of each group of surface electromyography data is calculated, and the combination of the feature extraction value algorithm corresponding to the maximum value in the average accuracy of the classification results and the pattern recognition classifier is taken as the optimal combination to determine what kind of gesture the surface electromyography signal belongs to. The embodiment of the invention adopts seven feature extraction combination algorithms and three classifiers for classification, thereby effectively improving the rapidity and stability of gesture recognition and bringing better user experience when applied to dexterous hand control.

Description

technical field [0001] The present invention relates to an optimized gesture recognition method based on surface electromyographic signals, which can be applied to the field of dexterous hand control of service robots. The sensor installed on the forearm can judge the user's movement intention in real time, and control the dexterous hand according to the user's intention. Get some exercise. Background technique [0002] Surface Electromyography (SEMG) is a bioelectrical signal related to neuromuscular activity. When the motor command is transmitted to the relevant muscle fibers through the nervous system, it will cause a change in the potential on the muscle fiber and the contraction of the muscle fiber. Electrode collection. The surface EMG signal contains information such as the mode and strength of muscle contraction. Different body movements correspond to different EMG signals. By analyzing the surface EMG signal, the specific action pattern corresponding to the signal...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F3/01G06K9/62G06N3/08B25J19/00
CPCG06F3/015G06F3/017G06N3/08B25J19/00G06F18/2411
Inventor 胡俊王宏玉刘世昌宋吉来朱洪彪陈禹希
Owner SHENYANG SIASUN ROBOT & AUTOMATION
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