基于9轴姿态传感器的CNN旗语动作识别方法

Method of CNN Flag Movement Recognition Based on 9-axis Attitude Sensor

区别于传统光纤传感器、图像识别和Kinect深度图像的旗语动作识别方法,提出了一种基于9轴姿态传感器的旗语动作识别方法。该方法通过佩戴在手腕处的9轴姿态传感器来采集旗语动作的3轴加速度、3轴角速度以及3轴磁偏角数据;在运用卷积神经网络(Convolutional Neural Network, CNN)分类模型的基础上对其中的数据进行预处理,并通过分类识别算法对其进行改进;在数据预处理阶段,利用小波分解与重构函数对采集到的9轴数据进行高频去噪和低频信息提取,通过时间序列加窗进行分割处理,对各动作样本进行维度和长度统一;在特征提取阶段,采用构建的双卷积层、单池化层、单全连接层网络模型对重构数据进行特征提取;在分类识别阶段,提出一种CrossEntropy-Logistic联合损失函数来对5种动作进行迭代训练。实验结果表明,所提方法利用detcoef小波分解与重构函数对信号进行低频细节系数提取并采用一维CNN对降噪后的数据进行特征提取,通过CL联合损失函数对预测损失值和预测概率进行融合,分析所得到的训练准确率与测试准确率,在与各类方法的对比中取得了最高值,其平均训练识别率可达99%以上,测试准确率可达94%。

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%.