关键词: 步态识别, 步态相位, 神经网络, 粒子群算法

Abstract: 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.

Key words: gait recognition, gait phase, neural network, particle swarm optimization