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DiamondNet: A Neural-Network-Based Heterogeneous Sensor Attentive Fusion for Human Activity Recognition

文献类型：期刊论文

作者Zhu, Yida2; Luo, Haiyong1; Chen, Runze2; Zhao, Fang2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2023-07-04
页码11
关键词Convolutional denoising autoencoders (CDAEs) global-attention mechanism graph convolutional networks human activity recognition (HAR) multisensor modality self-attention mechanism
ISSN号2162-237X
DOI10.1109/TNNLS.2023.3285547
英文摘要With the proliferation of intelligent sensors integrated into mobile devices, fine-grained human activity recognition (HAR) based on lightweight sensors has emerged as a useful tool for personalized applications. Although shallow and deep learning algorithms have been proposed for HAR problems in the past decades, these methods have limited capability to exploit semantic features from multiple sensor types. To address this limitation, we propose a novel HAR framework, DiamondNet, which can create heterogeneous multisensor modalities, denoise, extract, and fuse features from a fresh perspective. In DiamondNet, we leverage multiple 1-D convolutional denoising autoencoders (1-D-CDAEs) to extract robust encoder features. We further introduce an attention-based graph convolutional network to construct new heterogeneous multisensor modalities, which adaptively exploit the potential relationship between different sensors. Moreover, the proposed attentive fusion subnet, which jointly employs a global-attention mechanism and shallow features, effectively calibrates different-level features of multiple sensor modalities. This approach amplifies informative features and provides a comprehensive and robust perception for HAR. The efficacy of the DiamondNet framework is validated on three public datasets. The experimental results demonstrate that our proposed DiamondNet outperforms other state-of-the-art baselines, achieving remarkable and consistent accuracy improvements. Overall, our work introduces a new perspective on HAR, leveraging the power of multiple sensor modalities and attention mechanisms to significantly improve the performance.
资助项目National Key Research and Development Program[2022YFB3904700] ; National Natural Science Foundation of China[62261042] ; National Natural Science Foundation of China[62002026] ; Key Research Projects of the Joint Research Fund for Beijing Natural Science Foundation ; Fengtai Rail Transit Frontier Research Joint Fund[L221003] ; Beijing Natural Science Foundation[4232035] ; Beijing Natural Science Foundation[4212024] ; Beijing Natural Science Foundation[4222034] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA28040500] ; Fundamental Research Funds for the Central Universities[2022RC13] ; BUPT Excellent Ph.D. Students Foundation[CX2020220] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001025578500001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/21249]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Luo, Haiyong; Zhao, Fang
作者单位1.Chinese Acad Sci, Res Ctr Ubiquitous Comp Syst, Inst Comp Technol, Beijing 100190, Peoples R China
2.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Yida,Luo, Haiyong,Chen, Runze,et al. DiamondNet: A Neural-Network-Based Heterogeneous Sensor Attentive Fusion for Human Activity Recognition[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2023:11.
APA Zhu, Yida,Luo, Haiyong,Chen, Runze,&Zhao, Fang.(2023).DiamondNet: A Neural-Network-Based Heterogeneous Sensor Attentive Fusion for Human Activity Recognition.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,11.
MLA Zhu, Yida,et al."DiamondNet: A Neural-Network-Based Heterogeneous Sensor Attentive Fusion for Human Activity Recognition".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023):11.

入库方式： OAI收割

来源：计算技术研究所

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