Method for identifying surface electromyography (sEMG) on basis of empirical mode decomposition (EMD) sample entropy

Abstract

The invention provides a method for identifying surface electromyography (sEMG) on the basis of empirical mode decomposition (EMD) sample entropy. The method comprises the following steps: acquiring the corresponding sEMG from the related muscle tissue; performing EMD by using an actuating signal of the sEMG determined by energy threshold; adaptively selecting a plurality of intrinsic mode function (IMF) components comprising electromyographic signal effective information according to a frequency availability method; superposing the IMF components to serve as effective electromyographic signals and evaluating the sample entropy; and inputting the sample entropy serving as feature vector into a clustering classifier based on a spindle kernel clustering algorithm to realize identification on an upper limb multi-locomotion mode of the electromyographic signal. The sample entropy can disclose the complexity of the sEMG from a short time sequence, represents the tiny change condition of the electromyographic signal well, has high antijamming capability, simple algorithm and high calculation speed, and is particularly suitable for real-time processing of the electromyographic signal.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a method for pattern recognition of myoelectric signals, in particular to a method for recognizing patterns of upper limb multi-motion based on myoelectric signals, which is applied to a myoelectric prosthetic hand. Background technique [0002] Surface electromyography (sEMG) is a bioelectrical signal related to neuromuscular activity recorded from the surface of human skeletal muscle through surface electromyographic pickup electrodes, which contains a lot of information related to limb movement. Limb movements have different muscle contraction patterns, and the characteristics of EMG signals will also be different. Through the analysis of these characteristics, different movement patterns of the limbs can be distinguished. Therefore, it is not only widely used in clinical diagnosis, sports medicine and other fields, It has also become an ideal control signal for prosthetic con...

AI Technical Summary

Problems solved by technology

These nonlinear algorithms have solved the feature extraction problem of myoelectric signal very well. However, these feature extraction methods require long-term stable sEMG signals, and their anti-noise ability is weak, so they cannot control the myoelectric prosthetic hand well in real time.

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