%0 Journal Article %A SUN Nan1 %A 2 %A LUO Minzhou2 %A WANG Yucheng2 %A ZHAO Hanbin2 %T Human gait recognition based on MPSO-BP neural network method %D 2017 %R 10.3778/j.issn.1002-8331.1605-0090 %J Computer Engineering and Applications %P 121-125 %V 53 %N 21 %X To improve the accuracy rate of human gait phase recognition for controlling the exoskeleton robot, an approach based on Modified Particle Swarm Optimization algorithm-Back Propagation(MPSO-BP) neural network is utilized to divide three types of gait into different phases. Firstly, the MPSO-BP neural network classifier is constructed through regulating the learning factor adaptively, and then the classifier is trained using sample set containing multi-sensor information. Secondly, test the classifier on gait phase recognition in three types of human gait including walk, upstairs and sit-down. The experimental results show that the MPSO-BP neural network classifier can successfully increase the accuracy rate up to averaged 96% above, which is superior to the BP neural network and the particle swarm optimization BP neural network methods. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1605-0090