A Novel Deep Learning Bi-GRU-I Model for Real-Time Human Activity Recognition Using Inertial Sensors
文献类型：期刊论文
| 作者 | Tong, Lina1; Ma, Hanghang1; Lin, Qianzhi1; He, Jiaji1; Peng, Liang2
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| 刊名 | IEEE SENSORS JOURNAL
 |
| 出版日期 | 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. |
| WOS关键词 | LONG-TERM ; SMARTPHONE ; RADAR |
| 资助项目 | Major Scientific and Technological Innovation Projects in Shandong Province[2019JZZY011111] ; National Natural Science Foundation of China[U21A20479] |
| WOS研究方向 | Engineering ; Instruments & Instrumentation ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000770054800141 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Major Scientific and Technological Innovation Projects in Shandong Province ; National Natural Science Foundation of China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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