Method for constructing multi-channel myoelectricity feature image data set

Abstract

The invention relates to a method for constructing a multi-channel myoelectricity feature image data set. The method comprises the following steps: firstly, combining a threshold comparison method with a Butterworth filter to complete preprocessing of an original electromyographic signal; then, performing screening twice from a common time domain and frequency domain features, and selecting four non-redundant myoelectricity features for feature extraction; secondly, generating a myoelectricity image based on a mapping relation between a one-dimensional signal and a two-dimensional image; and finally, completing construction of a multi-channel myoelectricity feature image according to an image splicing mode. A myoelectricity image data set is trained through the deep learning network, so that the gesture recognition rate can be effectively improved. The multi-channel myoelectricity feature image has richer feature information, complementation among information can be completed through multiple features, and the final recognition rate is 8%-9% higher than that of a single-channel myoelectricity feature image.

Description

technical field [0001] The invention relates to the fields of signal processing and human-computer interaction, in particular to a bidirectional recursive myoelectric feature selection method based on electromyographic signals. Background technique [0002] With the development of science and technology, more and more scholars pay attention to the research and application of human-computer interaction. Among them, gesture recognition, as a main branch of human-computer interaction, is one of the important research topics in the field of human-computer interaction. Gestures in human-computer interaction have the advantages of convenience, strong interactivity, and rich expression content for users, and have always been the first choice in the field of human-computer interaction. Myoelectric signal is a kind of bioelectrical signal generated during human muscle activity, which is the comprehensive result of the conduction and superposition of action potentials of different mo...

AI Technical Summary

Problems solved by technology

Myoelectric signals are mainly used for gesture recognition by extracting myoelectric features, but the classification and recognition rate of myoelectric gestures based on traditional methods has been stagnant.

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