Lower limb motion mode identification method integrated with surface electromyography and acceleration signals

Abstract

The invention provides a lower limb motion mode identification method integrated with surface electromyography and acceleration signals. The method comprises steps that firstly, the surface electromyography and acceleration signals of a lower limb of a human body are acquired; the surface electromyography signal is decomposed through utilizing a local mean decomposition algorithm to acquire multiple product functions, according to the average Euclidean distance representing separation of different motions, the multi-scale permutation entropy of the first product function after decomposition through the local mean decomposition algorithm is determined, and the multi-scale permutation entropy of the first product function is extracted as surface EMG signal characteristics; importance of theentropy at different scales is calculated, the scale entropy is determined to form a 4-dimensional characteristic vector, and the 4-dimensional characteristic vector and a triaxial acceleration sequence entropy form a 7-dimensional characteristic vector; the 7-dimensional characteristic vector is inputted to a binary tree support vector machine improved according to the intra-class average Euclidean distance and inter-class sample distribution to carry out lower limb motion mode identification. The method is advantaged in that human body lower limb motion intention can be accurately identifiedin real time, and the identification result can be utilized for exoskeleton robot interaction control.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a human body lower limb movement pattern recognition method that combines surface electromyography (sEMG) and acceleration signals. The examples are for walking, going upstairs, going downstairs, standing to sitting, sitting to standing, standing to squatting Recognize the seven daily behaviors of squatting and standing, and achieve better recognition results. Background technique [0002] With the rapid development of society, the number of patients with unilateral lower limb motor function impairment caused by congenital environment and acquired diseases is increasing. Improving the quality of life of these patients and gradually restoring their ability to exercise has become a focus of social attention and a topic in the field of medical rehabilitation. However, due to the shortage of medical institutions, technicians and equipment, and the high cost, the development of rehab...

AI Technical Summary

Problems solved by technology

However, the simple time-domain characteristics of the acceleration signal cannot effectively reflect the difference between different motion modes of the lower limbs, while the sort entropy can reflect the complexity of the acceleration signals of different motion modes, which is beneficial to distinguish different motion modes

Images