Gesture recognition method based on variational mode decomposition and support vector machine

Abstract

The invention discloses a gesture recognition method based on variational mode decomposition and a support vector machine, which comprises the following steps: firstly, performing decomposition and noise reduction on surface electromyogram signals by using a variational mode decomposition algorithm, and meanwhile, realizing optimal selection of parameters of the variational mode decomposition algorithm by using an improved artificial bee colony optimization algorithm, thereby avoiding blindness of manual selection; then, on the basis of the decomposed variational mode components, 4-order autoregression model parameters and fuzzy entropy are extracted, a multi-scale feature set is constructed, and surface electromyogram signal features can be effectively extracted; and finally, performing classification and gesture recognition by using a multi-classifier constructed by a support vector machine optimized by an improved cuckoo algorithm, and realizing a relatively good classification effect under the condition that the number of samples is relatively small. According to the method, the parameter selection problem of the variational mode decomposition algorithm is solved, the characteristics of the electromyographic signals can be more effectively obtained, and the accuracy and speed of gesture recognition based on the surface electromyographic signals are improved.

Description

technical field

[0001] The invention relates to a gesture recognition method based on variational mode decomposition and support vector machine, which belongs to the field of surface electromyographic signal recognition. Background technique

[0002] With the development of interdisciplinary research, surface electromyography signal gesture recognition has been widely used in biomedicine, rehabilitation robots, and artificial intelligence. This research has developed into a popular research topic in the fields of biosignal processing and pattern recognition. The surface electromyography signal has the characteristics of nonlinearity, non-stationary, strong randomness, and weak signal, and the background noise will be added to the electromyography signal during the acquisition process. It is difficult to obtain the real structure of the surface electromyography signal data by common feature extraction methods , making pattern recognition difficult.

[0003] For the problem t...

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