In order to improve recognition precision for human posture

an improved particle swarm optimization（ PSO） algorithm neural network is proposed to recognize human posture. Acceleration sensor is used to collect acceleration information. Based on common feature sets

discrete coefficient and curve integral are added

as input of neural network. by Controlling probability

particles are operated by genetic operation when parameters of neural network are optimized by PSO algorithm and it will make the particles jump out of local the mininum value. The six postures are recognized through trained neural network. Experimental results show that the proposed algorithm can improve the convergence speed and the ability of global optimization. Compared with other classical algorithms

the proposed algorithm has higher recognition precision.