Abstract: Different from the traditional method of flag movement recognition of optical fiber sensor,image recognition and kinect depth image,this paper proposes a method of flag movement recognition based on 9-axis attitude sensor.The data of 3-axis acce-leration,3-axis angular velocity and 3-axis magnetic decrement are collected by wearing a 9-axis attitude sensor at the wrist;based on the CNN classification model,the algorithm of data preprocessing and classification recognition is improved;in the data preprocessing stage,the wavelet decomposition and reconstruction functions are used to carry out the high-frequency denoising and low-frequency information extraction of the collected 9-axis data,and the dimension and the length of each action sample are unified through time series windowing and segmentation;in the feature extraction stage,the constructed network models of double convolution layer,single pooling layer and single full connection layer are used to extract the features of the reconstructed data;in the stage of classification and recognition,a CrossEntropy-Logistic joint loss function is proposed to carry out iterative training for 5 actions.The experimental results show that the use of wavelet decomposition and reconstruction detcoef function coefficient of low frequency detail of signals is extracted by using one-dimensional CNN data feature extraction.The training and testing accuracy obtained by the fusion analysis of the predicted loss value and the predicted probability through CL joint loss function is the highest in comparison with various methods.The average training recognition rate can reach more than 99% and the testing accuracy can reach 94%.

Key words: 9-axis attitude sensor, CNN, CrossEntropy-Logistic, Flag action recognition, Wavelet decomposition and reconstruction