Institute of Computing Technology, Chinese Academy IR
Cross-position activity recognition with stratified transfer learning | |
Chen, Yiqiang1,2; Wang, Jindong1,2; Huang, Meiyu3; Yu, Han4 | |
2019-07-01 | |
发表期刊 | PERVASIVE AND MOBILE COMPUTING |
ISSN | 1574-1192 |
卷号 | 57页码:1-13 |
摘要 | Human activity recognition (HAR) aims to recognize the activities of daily living by utilizing the sensors attached to different body parts. HAR relies on the machine learning models trained using sufficient activity data. However, when the labels from a certain body position (i.e. target domain) are missing, how to leverage the data from other positions (i.e. source domain) to help recognize the activities of this position? This problem can be divided into two steps. Firstly, when there are several source domains available, it is often difficult to select the most similar source domain to the target domain. Secondly, with the selected source domain, we need to perform accurate knowledge transfer between domains in order to recognize the activities on the target domain. Existing methods only learn the global distance between domains while ignoring the local property. In this paper, we propose a Stratified Transfer Learning (STL) framework to perform both source domain selection and activity transfer. STL is based on our proposed Stratified distance to capture the local property of domains. STL consists of two components: 1) Stratified Domain Selection (STL-SDS), which can select the most similar source domain to the target domain; and 2) Stratified Activity Transfer (STL-SAT), which is able to perform accurate knowledge transfer. Extensive experiments on three public activity recognition datasets demonstrate the superiority of STL. (C) 2019 Elsevier B.V. All rights reserved. |
关键词 | Activity recognition Transfer learning Domain adaptation Pervasive computing |
DOI | 10.1016/j.pmcj.2019.04.004 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key RD Plan of China[2017YFB1002802] ; NSFC[61572471] ; NSFC[61472399] ; NSFC[61702520] ; Beijing Municipal Science & Technology Commission[Z171100000117017] |
WOS研究方向 | Computer Science ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Telecommunications |
WOS记录号 | WOS:000470252100001 |
出版者 | ELSEVIER SCIENCE BV |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.204/handle/2XEOYT63/4212 |
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 Key Lab Mobile Comp & Pervas Devices, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.China Acad Space Technol, Qian Xuesen Lab Space Technol, Beijing, Peoples R China 4.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore |
推荐引用方式 GB/T 7714 | Chen, Yiqiang,Wang, Jindong,Huang, Meiyu,et al. Cross-position activity recognition with stratified transfer learning[J]. PERVASIVE AND MOBILE COMPUTING,2019,57:1-13. |
APA | Chen, Yiqiang,Wang, Jindong,Huang, Meiyu,&Yu, Han.(2019).Cross-position activity recognition with stratified transfer learning.PERVASIVE AND MOBILE COMPUTING,57,1-13. |
MLA | Chen, Yiqiang,et al."Cross-position activity recognition with stratified transfer learning".PERVASIVE AND MOBILE COMPUTING 57(2019):1-13. |
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