Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

doi:10.3390/rs11091140

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition
	Luo, Haiyong 3; Men, Aidong 4; Zhao, Fang 2; Ye, Langlang 3; Wang, Qu 4; Ou, Changhai 1
刊名	REMOTE SENSING
	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
出版者	MDPI
WOS记录号	WOS:000469763600148
内容类型	期刊论文
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.