高技术通讯2016，Vol.26Issue(4)：374-380,7.DOI:10.3772/j.issn.1002-0470.2016.04.007
基于特征融合进行活动识别的DCNN 5法
A DCNN method for human activity recognition based on feature fusion
摘要
Abstract
An activity recognition model , with its input being the multi-channel time series signals obtained by wearable sensors and output being a predefined activity , was studied .It was pointed that extracting effective features from ac-tivity is a key in activity recognition .Most of the existing work relies on manual extraction of the features and the shallow learning structure , which makes the work complex and the recognition unaccurate .However , the convolu-tional neural network ( CNN) based on deep learning does not manually extract the time series signals , but auto-matically learns the best feature .At present , using convolutional neural network to process limited labeled data still has the overfitting problem .Therefore, a systematic feature learning method based on fusion characteristics was presented for activity recognition .The method uses the ImageNet16 to pre-train the original data set to fuse the ob-tained data with the original data , and puts the fused data and the corresponding tag into a supervised depth convo-lutional neural network (DCNN) to train the new system.In this system, the characteristics of learning and classi-fication are mutually reinforcing , which can not only deal with the problem of limited data from end to end , but also make the learning more discriminative .Compared with other methods , the overall accuracy of the proposed method is increased from 87%to 87.4%.关键词
融合特征/多通道时间序列/深度卷积神经网络( DCNN)/活动识别Key words
fusion feature/multichannel time sequence/deep convolutional neural network ( DCNN)/activi-ty recognition引用本文复制引用
王金甲,杨中玉..基于特征融合进行活动识别的DCNN 5法[J].高技术通讯,2016,26(4):374-380,7.基金项目
国家自然科学基金（61273019，61473339），河北省自然科学基金（F2013203368），河北省青年拨尖人才支持项目（［2013］17），河北省博士后专项资助（B2014010005）和中国博士后科学基金（2014M561202）资助项目。 （）
