标题:Human Activity Recognition Based on Wearable Sensor Using Hierarchical Deep LSTM Networks
作者:Wang, LuKun ;Liu, RuYue
作者机构:[Wang, LuKun ;Liu, RuYue ] Department of Information Engineering, Shandong University of Science and Technology, Taian; 271019, China;[Wang, LuKun ] C 更多
通讯作者:Wang, LuKun
来源:Circuits, Systems, and Signal Processing
出版年:2020
卷:39
期:2
页码:837-856
DOI:10.1007/s00034-019-01116-y
摘要:In recent years, with the rapid development of artificial intelligence, human activity recognition has become a research focus. The complex, dynamic and variable features of human activities lead to the relatively low accuracy of the traditional recognition algorithms. In order to solve the problem, this paper will propose a novel structure named hierarchical deep LSTM (H-LSTM) based on long short-term memory. Firstly, the original sensor data are preprocessed by smoothing and denoising; then, the feature will be selected and extracted by time–frequency-domain method. Secondly, H-LSTM is applied to the classification of these activities. Three public UCI datasets are used to conduct simulation with the realization of the automatic extraction of feature vectors and classification of outputting recognition results. Finally, the simulation results testify to the outperformance of the H-LSTM network over other deep learning algorithms. The accuracy of H-LSTM network in human activity recognition is proved to be 99.15%.
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.
收录类别:EI
资源类型:期刊论文
TOP