Method and system for gesture recognition based on surface electromyogram signals
An electromyographic signal and gesture recognition technology, applied in the fields of signal processing and human-computer interaction, can solve the problems of gesture recognition that cannot adapt to the characteristics of signal distribution, data self-adaptation, and low accuracy of gesture recognition.
Active Publication Date: 2021-05-11
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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In the traditional gesture recognition method, the feature set is usually predefined to learn the feature expression of the gesture, but the optimal gesture feature set is often different due to the characteristics of the gesture signal, which cannot adapt to the signal distribution characteristics, nor can it realize the data-adaptive gesture recognition, resulting in Gesture recognition is less accurate
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[0025] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings. It should be understood that the described embodiments are some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0026] Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided in order to give a thorough understanding of embodiments of the invention. However, those skilled in the art will appreciate that the technical solutions of the present invention may be practiced without one or...
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The invention provides a method and system for gesture recognition based on surface electromyogram signals, and the method comprises the steps: extracting the spatial features and time features of a surface electromyogram signal at each moment through a convolutional recurrent neural network, and carrying out the weighted fusion of the time features through an attention mechanism, so as to predict the gesture type of a user. According to the gesture recognition method, the effective feature expression of the surface electromyogram signals can be learned in a self-adaptive mode, and high-precision recognition of the gestures of the user is achieved.
Description
technical field [0001] The invention relates to the technical fields of signal processing and human-computer interaction, in particular to a method and system for gesture recognition based on surface electromyographic signals. Background technique [0002] Surface Electromyographic (sEMG) is a common bioelectrical signal that can sense and analyze user muscle activity non-invasively. In view of its intuition and effectiveness in muscle activity perception, sEMG has shown a good application prospect in the field of gesture recognition. The accuracy of surface EMG gesture recognition systems is highly dependent on the choice of feature set. However, in the field of machine learning such as gesture recognition and behavior recognition, the effective feature set often varies due to different signal characteristics. In the traditional gesture recognition method, the feature set is usually predefined to learn the feature expression of the gesture, but the optimal gesture feature...
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Login to View More IPC IPC(8): G06F3/01G06K9/62G06N3/04G06N3/08
CPCG06F3/015G06F3/017G06N3/04G06N3/08G06F2203/011G06F18/2411G06F18/24323G06F18/25G06F18/214
Inventor 陈益强张迎伟于汉超杨晓东卢旺孙睿哲杨威文
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI