Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition
文献类型：期刊论文
| 作者 | Luo, Haiyong3; Men, Aidong4; Zhao, Fang2; Ye, Langlang3; Wang, Qu4; Ou, Changhai1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2019-05-01 |
| 卷号 | 11期号:9页码:23 |
| 关键词 | indoor positioning machine learning pedestrian dead reckoning stride length estimation smartphone mode recognition |
| ISSN号 | 2072-4292 |
| DOI | 10.3390/rs11091140 |
| 英文摘要 | Stride length and walking distance estimation are becoming a key aspect of many applications. One of the methods of enhancing the accuracy of pedestrian dead reckoning is to accurately estimate the stride length of pedestrians. Existing stride length estimation (SLE) algorithms present good performance in the cases of walking at normal speed and the fixed smartphone mode (handheld). The mode represents a specific state of the carried smartphone. The error of existing SLE algorithms increases in complex scenes with many mode changes. Considering that stride length estimation is very sensitive to smartphone modes, this paper focused on combining smartphone mode recognition and stride length estimation to provide an accurate walking distance estimation. We combined multiple classification models to recognize five smartphone modes (calling, handheld, pocket, armband, swing). In addition to using a combination of time-domain and frequency-domain features of smartphone built-in accelerometers and gyroscopes during the stride interval, we constructed higher-order features based on the acknowledged studies (Kim, Scarlett, and Weinberg) to model stride length using the regression model of machine learning. In the offline phase, we trained the corresponding stride length estimation model for each mode. In the online prediction stage, we called the corresponding stride length estimation model according to the smartphone mode of a pedestrian. To train and evaluate the performance of our SLE, a dataset with smartphone mode, actual stride length, and total walking distance were collected. We conducted extensive and elaborate experiments to verify the performance of the proposed algorithm and compare it with the state-of-the-art SLE algorithms. Experimental results demonstrated that the proposed walking distance estimation method achieved significant accuracy improvement over existing individual approaches when a pedestrian was walking in both indoor and outdoor complex environments with multiple mode changes. |
| 资助项目 | National Key Research and Development Program[2018YFB0505200] ; BUPT Excellent Ph.D. Students Foundation[CX2018102] ; National Natural Science Foundation of China[61872046] ; National Natural Science Foundation of China[61374214] ; National Natural Science Foundation of China[61671264] ; National Natural Science Foundation of China[61671077] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000469763600148 |
| 出版者 | MDPI |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/4213]  |
| 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.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore 2.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 4.Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China |
| 推荐引用方式 GB/T 7714 | Luo, Haiyong,Men, Aidong,Zhao, Fang,et al. Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition[J]. REMOTE SENSING,2019,11(9):23. |
| APA | Luo, Haiyong,Men, Aidong,Zhao, Fang,Ye, Langlang,Wang, Qu,&Ou, Changhai.(2019).Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition.REMOTE SENSING,11(9),23. |
| MLA | Luo, Haiyong,et al."Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition".REMOTE SENSING 11.9(2019):23. |
入库方式： OAI收割
来源：计算技术研究所
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