.png){kind=icon}
返回
Personalized Stride-Length Estimation Based on Active Online Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2020.2971318.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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%.
Keyword:
Indoor positioning
Internet of Things (IoT)
online learning
pedestrian dead reckoning (PDR)
stride-length estimation (SLE)
walking-distance estimation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
Nanostructured hybrid NiFeOOH/CNT electrocatalysts for oxygen evolution reaction with low overpotential.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏A unified approach for process‐based hydrologic modeling: 2. Model implementation and case studies
OAAI
OAAI分享收藏Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor LocalizationSENSORSOAAI
SENSORSIF3.5
OAAI分享收藏A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBMSENSORSOAAI
SENSORSIF3.5
OAAI分享收藏
查看更多内容