计算机应用研究2017，Vol.34Issue(12)：3569-3572,4.DOI:10.3969/j.issn.1001-3695.2017.12.011
CNN与决策树结合的新型人体行为识别方法研究
Research on new human behavior recognition method based on CNN and decision tree
摘要
Abstract
The utilization of smart phone'acceleration sensors to identify human behavior is a big topic in intelligence field.Traditional identification methods,such as Bayes,speed learning and decision tree,must first collected acquisition frequency domain features of acceleration sensor data and extracted preferred features.This paper utilized convolutional neural network algorithm to extract smart phone'accelerate data in three dimensions,and then automatically found out patterns from the data.Finally it used decision tree to identify human behavior from the pattern.Experimental results show that recognition accuracy improves 1.1％ to 5.2％ in comparison with traditional machine learning methods,especially data set on the large-scale.关键词
行为识别/深度学习/卷积神经网络/决策树/特征提取Key words
behavior recognition/deep learning/convolutional neural network (CNN)/decision tree/feature extraction分类
信息技术与安全科学引用本文复制引用
王忠民,张琮,衡霞..CNN与决策树结合的新型人体行为识别方法研究[J].计算机应用研究,2017,34(12):3569-3572,4.基金项目
国家自然科学基金资助项目(61373116) （61373116）
陕西省科技统筹创新工程计划项目(2016KTZDGY04-01) （2016KTZDGY04-01）
陕西省教育厅专项科研计划资助项目(16JK1706) （16JK1706）
西安市科技局科技计划项目(2017084CG/RC047(XAYD001) （2017084CG/RC047(XAYD001）
