OKRELM: online kernelized and regularized extreme learning machine for wearable-based activity recognition

doi:10.1007/s13042-017-0666-8

	OKRELM: online kernelized and regularized extreme learning machine for wearable-based activity recognition
	Hu, Lisha 1,2,3; Chen, Yiqiang 1,2,3; Wang, Jindong 1,2,3; Hu, Chunyu 1,2,3; Jiang, Xinlong 1,2,3
	2018-09-01
发表期刊	INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
ISSN	1868-8071
卷号	9 期号:9 页码:1577-1590
摘要	Miscellaneous mini-wearable devices (Jawbone Up, Apple Watch, Google Glass, et al.) have emerged in recent years to recognize the user's activities of daily living (ADLs) such as walking, running, climbing and bicycling. To better suits a target user, a generic activity recognition (AR) model inside the wearable devices requires to adapt itself according to the user's personality in terms of wearing styles and so on. In this paper, an online kernelized and regularized extreme learning machine (OKRELM) is proposed for wearable-based activity recognition. A small-scale but important subset of every incoming data chunk is chosen to go through the update stage during the online sequential learning. Therefore, OKRELM is a lightweight incremental learning model with less time consumption during the update and prediction phase, a robust and effective classifier compared with the batch learning scheme. The performance of OKRELM is evaluated and compared with several related approaches on a UCI online available AR dataset and experimental results show the efficiency and effectiveness of OKRELM.
关键词	Extreme learning machine Kernel Activity recognition Online learning Wearable computing
DOI	10.1007/s13042-017-0666-8
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[61572471] ; Natural Science Foundation of China[61210010] ; Chinese Academy of Sciences Research Equipment Development Project[YZ201527] ; Science and Technology Planning Project of Guangdong Province[2015B010105001]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000441128800013
出版者	SPRINGER HEIDELBERG
引用统计	被引频次：20[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/2XEOYT63/5062
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chen, Yiqiang
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Beijing Key Lab Mobile Comp & Pervas Device, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Hu, Lisha,Chen, Yiqiang,Wang, Jindong,et al. OKRELM: online kernelized and regularized extreme learning machine for wearable-based activity recognition[J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,2018,9(9):1577-1590.
APA	Hu, Lisha,Chen, Yiqiang,Wang, Jindong,Hu, Chunyu,&Jiang, Xinlong.(2018).OKRELM: online kernelized and regularized extreme learning machine for wearable-based activity recognition.INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,9(9),1577-1590.
MLA	Hu, Lisha,et al."OKRELM: online kernelized and regularized extreme learning machine for wearable-based activity recognition".INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 9.9(2018):1577-1590.