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

doi:10.1109/JSEN.2022.3148431

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	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, Liang 2
刊名	IEEE SENSORS JOURNAL
	2022-03-15
卷号	22 期号:6 页码:6164-6174
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
ISSN号	1530-437X
DOI	10.1109/JSEN.2022.3148431
通讯作者	Peng, Liang(liang.peng@ia.ac.cn)
英文摘要	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.
资助项目	Major Scientific and Technological Innovation Projects in Shandong Province[2019JZZY011111] ; National Natural Science Foundation of China[U21A20479]
WOS关键词	LONG-TERM ; SMARTPHONE ; RADAR
WOS研究方向	Engineering ; Instruments & Instrumentation ; Physics
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000770054800141
资助机构	Major Scientific and Technological Innovation Projects in Shandong Province ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48163]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
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
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.