Personalized Stride-Length Estimation Based on Active Online Learning
文献类型：期刊论文
| 作者 | Wang, Qu1; Luo, Haiyong2; Ye, Langlang2; Men, Aidong1; Zhao, Fang3; Huang, Yan4; Ou, Changhai5 |
| 刊名 | IEEE INTERNET OF THINGS JOURNAL
 |
| 出版日期 | 2020-06-01 |
| 卷号 | 7期号:6页码:4885-4897 |
| 关键词 | Indoor positioning Internet of Things (IoT) online learning pedestrian dead reckoning (PDR) stride-length estimation (SLE) walking-distance estimation |
| ISSN号 | 2327-4662 |
| DOI | 10.1109/JIOT.2020.2971318 |
| 英文摘要 | The ability to accurately estimate a user's stride length plays a great important role in various applications. For a new target pedestrian or device, their heterogeneity dramatically reduces the performance of the current stride-length estimation (SLE) methods. To address the issue of heterogeneity, in this article, we propose an SLE method based on a long short-term memory (LSTM) network and denoising autoencoders (DAEs). The LSTM network is used to mine temporal dependencies and extract significant eigenvectors from the corrupted inertial sensor observations. Then, DAEs are adopted to automatically eliminate the inherent noise in eigenvectors and obtain denoised eigenvectors. Finally, a regression module maps the denoised eigenvectors to the resulting stride length. To mitigate the heterogeneity, we propose an unperceived model updating framework based on active online learning to establish a personalized model for a given target pedestrian or device. The proposed framework utilizes a magnetism-aided map-matching approach to automatically generate personalized training data and utilizes online learning technologies to evolve the stride-length model. The extensive experimental results demonstrate that the proposed method outperforms other state-of-the-art algorithms and achieves a promising accuracy with a stride-length error rate of 4.59% at a confidence level of 80%. |
| 资助项目 | National Key Research and Development Program[2016YFB0502000] ; Action Plan Project of the Beijing University of Posts and Telecommunications - Fundamental Research Funds for the Central Universities[2019XD-A06] ; Special Project for Youth Research and Innovation, Beijing University of Posts and Telecommunications ; Fundamental Research Funds for the Central Universities[2019PTB-011] ; National Natural Science Foundation of China[61872046] ; National Natural Science Foundation of China[61761038] ; National Natural Science Foundation of China[61671264] ; National Natural Science Foundation of China[61671077] ; Key Research and Development Project from Hebei Province[19210404D] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000543157700016 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/15196]  |
| 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 |
| 作者单位 | 1.Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China 2.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China 3.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China 4.Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China 5.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore |
| 推荐引用方式 GB/T 7714 | Wang, Qu,Luo, Haiyong,Ye, Langlang,et al. Personalized Stride-Length Estimation Based on Active Online Learning[J]. IEEE INTERNET OF THINGS JOURNAL,2020,7(6):4885-4897. |
| APA | Wang, Qu.,Luo, Haiyong.,Ye, Langlang.,Men, Aidong.,Zhao, Fang.,...&Ou, Changhai.(2020).Personalized Stride-Length Estimation Based on Active Online Learning.IEEE INTERNET OF THINGS JOURNAL,7(6),4885-4897. |
| MLA | Wang, Qu,et al."Personalized Stride-Length Estimation Based on Active Online Learning".IEEE INTERNET OF THINGS JOURNAL 7.6(2020):4885-4897. |
入库方式： OAI收割
来源：计算技术研究所
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