Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition

doi:10.1016/j.inffus.2021.10.009

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 > 先进控制与自动化团队

	Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition
	Zheng, Wenbo 1,4; Yan, Lan 1,2; Gou, Chao 3; Wang, Fei-Yue 1
刊名	INFORMATION FUSION
	2022-04-01
卷号	80 页码:1-22
关键词	Meta-learning Internet of Things Graph model Attention mechanisms
ISSN号	1566-2535
DOI	10.1016/j.inffus.2021.10.009
通讯作者	Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
英文摘要	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.
资助项目	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]
WOS关键词	RECURRENT NEURAL-NETWORK ; PRIORITY MAPS ; DATA FUSION ; WEARABLE SENSOR ; ATTENTION ; MATRIX
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000724320000001
资助机构	National Key R&D Program of China ; Key Research and De-velopment Program of Guangzhou ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46782]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Wang, Fei-Yue
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 3.Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510275, Peoples R China 4.Xi An Jiao Tong Univ, Sch Software Engn, Xian 710049, Peoples R China
推荐引用方式 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.