| Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition | |
Zheng, Wenbo1,2; Yan, Lan1,3 ; Gou, Chao4; Wang, Fei-Yue1
| |
| Source Publication | INFORMATION FUSION
 |
| ISSN | 1566-2535 |
| 2022-04-01 | |
| Volume | 80Pages:1-22 |
| Corresponding Author | Wang, Fei-Yue(feiyue.wang@ia.ac.cn) |
| Abstract | With the rapid growth of the Internet of Things (IoT), smart systems and applications are equipped with an increasing number of wearable sensors and mobile devices. These sensors are used not only to collect data but, more importantly, to assist in tracking and analyzing the daily human activities. Sensor-based human activity recognition is a hotspot and starts to employ deep learning approaches to supersede traditional shallow learning that rely on hand-crafted features. Although many successful methods have been proposed, there are three challenges to overcome: (1) deep model's performance overly depends on the data size; (2) deep model cannot explicitly capture abundant sample distribution characteristics; (3) deep model cannot jointly consider sample features, sample distribution characteristics, and the relationship between the two. To address these issues, we propose a meta-learning-based graph prototypical model with priority attention mechanism for sensor-based human activity recognition. This approach learns not only sample features and sample distribution characteristics via meta-learning-based graph prototypical model, but also the embeddings derived from priority attention mechanism that mines and utilizes relations between sample features and sample distribution characteristics. What is more, the knowledge learned through our approach can be seen as a priori applicable to improve the performance for other general reasoning tasks. Experimental results on fourteen datasets demonstrate that the proposed approach significantly outperforms other state-of-the-art methods. On the other hand, experiments of applying our model to two other tasks show that our model effectively supports other recognition tasks related to human activity and improves performance on the datasets of these tasks. |
| Keyword | Meta-learning Internet of Things Graph model Attention mechanisms |
| DOI | 10.1016/j.inffus.2021.10.009 |
| WOS Keyword | RECURRENT NEURAL-NETWORK ; PRIORITY MAPS ; DATA FUSION ; WEARABLE SENSOR ; ATTENTION ; MATRIX |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Key R&D Program of China[2020YFB1600400] ; National Key R&D Program of China[2018AAA0101502] ; Key Research and De-velopment Program of Guangzhou[202007050002] ; National Natural Science Foundation of China[61806198] ; National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[U1811463] |
| Funding Organization | National Key R&D Program of China ; Key Research and De-velopment Program of Guangzhou ; National Natural Science Foundation of China |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods |
| WOS ID | WOS:000724320000001 |
| Publisher | ELSEVIER |
| Sub direction classification | 生物特征识别 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/46783 |
| Collection | 复杂系统管理与控制国家重点实验室_平行智能技术与系统团队 |
| Corresponding Author | Wang, Fei-Yue |
| Affiliation | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Xi An Jiao Tong Univ, Sch Software Engn, Xian 710049, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 4.Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510275, Peoples R China |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Zheng, Wenbo,Yan, Lan,Gou, Chao,et al. Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition[J]. INFORMATION FUSION,2022,80:1-22. |
| APA | Zheng, Wenbo,Yan, Lan,Gou, Chao,&Wang, Fei-Yue.(2022).Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition.INFORMATION FUSION,80,1-22. |
| MLA | Zheng, Wenbo,et al."Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition".INFORMATION FUSION 80(2022):1-22. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment