A Novel Deep Learning Bi-GRU-I Model for Real-Time Human Activity Recognition Using Inertial Sensors

doi:10.1109/JSEN.2022.3148431

	A Novel Deep Learning Bi-GRU-I Model for Real-Time Human Activity Recognition Using Inertial Sensors
	Tong, Lina 1; Ma, Hanghang 1; Lin, Qianzhi 1; He, Jiaji 1; Peng, Liang2
发表期刊	IEEE SENSORS JOURNAL
ISSN	1530-437X
	2022-03-15
卷号	22 期号:6 页码:6164-6174
通讯作者	Peng, Liang(liang.peng@ia.ac.cn)
摘要	Wearable sensor based Human Activity Recognition (HAR) has been widely used these years. This paper proposed a novel deep learning model for HAR using inertial sensors. First, a wearable device platform was developed with 6 inertial sensor units to collect triaxial acceleration signals during human movements, and the dataset of Command Actions of Traffic Police (CATP) was acquired. Then, a deep learning model named Bidirectional-Gated Recurrent Unit-Inception (Bi-GRU-I) was designed to improve the accuracy and reduce the amount of parameters. It is consisting of 2 Bi-GRU layers, 3 Inception layers, 1 Global Average Pooling (GAP) layer and 1 softmax layer. Finally, the comparing experiments with other methods were taken on 3 datasets: the self-collected CATP dataset, widely used Wireless Sensor Data Mining (WISDM) and University of California, Irvine (UCI-HAR) dataset. And the proposed method shows better performance and robustness. Moreover, the sensor configuration optimization was analyzed, and it shows that this method can also apply to the task using less sensor units.
关键词	Sensors Feature extraction Deep learning Inertial sensors Data mining Convolutional neural networks Accelerometers Human activity recognition (HAR) inertial sensor deep learning Bi-GRU inception architecture
DOI	10.1109/JSEN.2022.3148431
关键词[WOS]	LONG-TERM ; SMARTPHONE ; RADAR
收录类别	SCI
语种	英语
资助项目	Major Scientific and Technological Innovation Projects in Shandong Province[2019JZZY011111] ; National Natural Science Foundation of China[U21A20479]
项目资助者	Major Scientific and Technological Innovation Projects in Shandong Province ; National Natural Science Foundation of China
WOS研究方向	Engineering ; Instruments & Instrumentation ; Physics
WOS类目	Engineering, Electrical & Electronic ; Instruments & Instrumentation ; Physics, Applied
WOS记录号	WOS:000770054800141
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	多模态智能
引用统计	被引频次：35[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48164
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Peng, Liang
作者单位	1.China Univ Min & Technol Beijing, Elect Engn & Automat Dept, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst S, Beijing 100190, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Tong, Lina,Ma, Hanghang,Lin, Qianzhi,et al. A Novel Deep Learning Bi-GRU-I Model for Real-Time Human Activity Recognition Using Inertial Sensors[J]. IEEE SENSORS JOURNAL,2022,22(6):6164-6174.
APA	Tong, Lina,Ma, Hanghang,Lin, Qianzhi,He, Jiaji,&Peng, Liang.(2022).A Novel Deep Learning Bi-GRU-I Model for Real-Time Human Activity Recognition Using Inertial Sensors.IEEE SENSORS JOURNAL,22(6),6164-6174.
MLA	Tong, Lina,et al."A Novel Deep Learning Bi-GRU-I Model for Real-Time Human Activity Recognition Using Inertial Sensors".IEEE SENSORS JOURNAL 22.6(2022):6164-6174.