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] Shandong Univ Sci & Technol, Dept Informat Engn, Tai An 271019, Shandong, Peoples R China.; [Wang, LuKun] Shandong Univ Sc 更多
通讯作者:Wang, LuKun
通讯作者地址:Wang, LK (corresponding author), Shandong Univ Sci & Technol, Dept Informat Engn, Tai An 271019, Shandong, Peoples R China.; Wang, LK (corresponding a 更多
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
关键词:Human activity recognition; Acceleration sensor; Recurrent neural; network (RNN); Long short-term memory (LSTM)
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%.
收录类别:EI;SCIE
WOS核心被引频次:1
资源类型:期刊论文
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