Aiming at the problem that the accuracy of pedestrian gait recognition is reduced due to the interference of sensors caused by different positions of mobile phones,a particle swarm optimization-extreme learning machine(PSO-ELM)recognition algorithm is proposed.Firstly,based on the classification method of ELM and the characteristics of multi-level dimension reduction of hierarchical ELM,PSO algorithm is used to optimize the parameters of ELM model,and a hierarchical PSO-ELM classification method is designed to effectively identify the location of pedestrians'mobile phones.Then,through the dimensionality reduction algorithm of linear discriminant analysis and PSO-ELM,the effective recognition of pedestrian gait is completed.In the experiments,the acceleration and angular velocity data under four gaits of five carrying positions are collected by Android phones.The results show that the recognition accuracy of the training set and the test set are 99.54%and 99.47%at the level of identifying the carrying position of the mobile phone.At the level of identifying pedestrian gait,the accuracy of the two sets reach 95.74%and 95.31%,which proves that the proposed algorithm has high gait recognition accuracy.
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