Bi-STAN: bilinear spatial-temporal attention network for wearable human activity recognition

doi:10.1007/s13042-023-01781-1

	Bi-STAN: bilinear spatial-temporal attention network for wearable human activity recognition
	Gao, Chenlong 1,2,3; Chen, Yiqiang 1,2,3; Jiang, Xinlong 1,2,3; Hu, Lisha ; Zhao, Zhicheng 4,5,6; Zhang, Yuxin 7
	2023-02-02
发表期刊	INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
ISSN	1868-8071
页码	17
摘要	With the progressive development of ubiquitous computing, wearable human activity recognition is playing an increasingly important role in many fields, such as health monitoring, disease-assisted diagnostic rehabilitation, and exercise assessment. Internal measurement unit in wearable devices provides a rich representation of motion. Human activity recognition based on sensor sequence has proven to be crucial in machine learning research. The key challenge is to extract powerful representational features from multi-sensor data to capture subtle differences in human activities. Beyond this challenge, due to the lack of attention to the temporal and spatial dependence of the data, critical information is often lost in the feature extraction process. Few previous papers can jointly address these two challenges. In this paper, we propose an efficient Bilinear Spatial-Temporal Attention Network (Bi-STAN). Firstly, a multi-scale ResNet backbone network is used to extract multimodal signal features and jointly optimize the feature extraction process. Then, to adaptively focus on what and where is important in the original data and to mine the discriminative part of the features, we design a spatial-temporal attention network. Finally, a bilinear pooling with low redundancy is introduced to efficiently obtain second-order information. Experiments on three public datasets and our real-world dataset demonstrate that the proposed Bi-STAN is superior to existing methods in terms of both accuracy and efficiency.
关键词	Human activity recognition Spatial-temporal attention Bilinear pooling Low-redundancy
DOI	10.1007/s13042-023-01781-1
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Plan of China[2021YFC2501202] ; Natural Science Foundation of China[61972383] ; ~Beijing Municipal Science & Technology Commission[Z221100002722009] ; ~Youth Innovation Promotion Association CAS ; Science and Technology Research Project of Higher Education of Hebei Province[QN2023184]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000923910600001
出版者	SPRINGER HEIDELBERG
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19953
专题	中国科学院计算技术研究所期刊论文
通讯作者	Chen, Yiqiang
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 3.Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 4.Hebei Univ Econ & Business, Inst Informat Technol, Shijiazhuang 050061, Peoples R China 5.Anhui Univ, Sch Artificial Intelligence, Hefei 230601, Peoples R China 6.China Elect Technol Grp Corp, Res Inst 38, Hefei 230088, Peoples R China 7.Global Energy Interconnect Dev & Cooperat Org, Beijing 100031, Peoples R China
推荐引用方式 GB/T 7714	Gao, Chenlong,Chen, Yiqiang,Jiang, Xinlong,et al. Bi-STAN: bilinear spatial-temporal attention network for wearable human activity recognition[J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,2023:17.
APA	Gao, Chenlong,Chen, Yiqiang,Jiang, Xinlong,Hu, Lisha,Zhao, Zhicheng,&Zhang, Yuxin.(2023).Bi-STAN: bilinear spatial-temporal attention network for wearable human activity recognition.INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,17.
MLA	Gao, Chenlong,et al."Bi-STAN: bilinear spatial-temporal attention network for wearable human activity recognition".INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2023):17.