Adaboost-SSA-BP gait recognition method based on the fusion of information entropy and improved feature weight
Aiming at the problem of high error rate of gait recognition using acceleration signals,an Adaboost-SSA-BP gait recognition method based on the fusion of information entropy and improved feature weight is proposed.The features for the outputs from the accelerometer under different gait are extracted based on the information entropy theory and the improved feature weight method algorithm,and then the features are combined for the gait recognition.The BP neural network is optimized by SSA,the weight of the optimized network is adjusted by Adaboost algorithm,and the gait recognition model is obtained by iterative training.Experiment results show that the method can effectively capture gait features,and the average accuracy of gait recognition reaches 96.15%.It can provide the technical support for the related researches such as the gait rehabilitation training.
NETL
NSTL
万方数据
维普
