Adaptive coefficient-based kernelized network for personalized activity recognition

doi:10.1007/s13042-021-01455-w

	Adaptive coefficient-based kernelized network for personalized activity recognition
	Hu, Lisha 1; Hu, Chunyu 2,3; Jiang, Xinlong 4; Huo, Zheng 1
刊名	INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
	2021-10-26
页码	23
关键词	Incremental learning Activity recognition Neural network Wearable device
ISSN号	1868-8071
DOI	10.1007/s13042-021-01455-w
英文摘要	Human activity recognition (HAR) based on wearable devices has found wide applications in fitness, health care, etc. Given the personalized wearing styles of such devices and distinctive motion patterns, the activities of daily living normally vary from person to person in terms of strength, amplitude, speed, category, etc. The specialization of a universal HAR model to a specific subject without experiencing catastrophic forgetting is a significant challenge. In this paper, we propose a novel incremental learning method, namely, an adaptive coefficient-based kernelized and regularized network (KeRNet-AC), for personalized activity recognition. During the adaptation stage of the model training process, KeRNet-AC consistently monitors the probable ill-conditioned degree of the generated solution, which we believe is strongly correlated with the catastrophic forgetting problem, and automatically makes the solution well conditioned. To reduce the computational complexity of KeRNet-AC, we also introduce an active data selection principle into KeRNet-AC. This variation is called A-KeRNet-AC. To evaluate the performance of KeRNet-AC and A-KeRNet-AC, we conduct extensive experiments on five public activity datasets. The experimental results demonstrate that KeRNet-AC outperforms related state-of-the-art methods in most cases and that A-KeRNet-AC can quickly perform model training and activity prediction. Moreover, the performance of the proposed methods steadily improves during the adaptation stage and ultimately converges without degradation, demonstrating the strong potential of KeRNet-AC and A-KeRNet-AC for personalized activity recognition.
资助项目	National Key Research and Development Plan of China[2018YFC2002603] ; National Natural Science Foundation of China[62002187] ; National Natural Science Foundation of China[62002098] ; National Natural Science Foundation of China[61902379] ; Natural Science Foundation of Hebei Province[2019207061] ; Natural Science Foundation of Hebei Province[2020207001] ; Natural Science Foundation of Hebei Province[2021207005] ; Science and Technology Research Project of Higher Education of Hebei Province[QN2018116] ; Research Foundation of Hebei University of Economics and Business[2018QZ04] ; Research Foundation of Hebei University of Economics and Business[2019JYQ08]
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER HEIDELBERG
WOS记录号	WOS:000711374400001
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/16923]
专题	中国科学院计算技术研究所
通讯作者	Hu, Chunyu
作者单位	1.Hebei Univ Econ & Business, Inst Informat Technol, Shijiazhuang 050061, Hebei, Peoples R China 2.Qilu Univ Technol, Sch Comp Sci & Technol, Shandong Acad Sci, Jinan 250353, Peoples R China 3.Shandong Prov Key Lab Distributed Comp Software N, Jinan 250353, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Hu, Lisha,Hu, Chunyu,Jiang, Xinlong,et al. Adaptive coefficient-based kernelized network for personalized activity recognition[J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,2021:23.
APA	Hu, Lisha,Hu, Chunyu,Jiang, Xinlong,&Huo, Zheng.(2021).Adaptive coefficient-based kernelized network for personalized activity recognition.INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,23.
MLA	Hu, Lisha,et al."Adaptive coefficient-based kernelized network for personalized activity recognition".INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2021):23.