In order to improve the accuracy of gait phase recognition for lower limb exoskeleton robots

a method based on the improved giant armadillo optimization (IGAO) algorithm is introduced to optimize the support vector machine (SVM).In this method

the optimization initialization strategy based on sinusoidal chaotic map and the Cauchy mutation strategy are integrated into the traditional giant armadillo optimization (GAO) algorithm

and a stochastic perturbation mechanism is introduced to enhance the global search ability of the algorithm and optimize the parameter configuration of the SVM.After evaluation on four typical test functions

the IGAO algorithm shows superior performance compared with other algorithms in terms of optimization precision

convergence speed

and algorithm stability.The practical application tests on the exoskeleton robot further verify the significant improvement in gait detection accuracy of this method

and prove its effectiveness in gait recognition of lower limb exoskeleton robots.