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 PublicationINFORMATION FUSION
ISSN1566-2535
2022-04-01
Volume80Pages:1-22
Corresponding AuthorWang, Fei-Yue(feiyue.wang@ia.ac.cn)
AbstractWith 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.
KeywordMeta-learning Internet of Things Graph model Attention mechanisms
DOI10.1016/j.inffus.2021.10.009
WOS KeywordRECURRENT NEURAL-NETWORK ; PRIORITY MAPS ; DATA FUSION ; WEARABLE SENSOR ; ATTENTION ; MATRIX
Indexed BySCI
Language英语
Funding ProjectNational 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 OrganizationNational Key R&D Program of China ; Key Research and De-velopment Program of Guangzhou ; National Natural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS IDWOS:000724320000001
PublisherELSEVIER
Sub direction classification生物特征识别
Citation statistics
Document Type期刊论文
Identifierhttp://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 AuthorWang, Fei-Yue
Affiliation1.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 AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute 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.
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