Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders

doi:10.3390/s19040840

	Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders
	Wang, Qu 1; Ye, Langlang 2; Luo, Haiyong 2; Men, Aidong 1; Zhao, Fang 3; Huang, Yan 4
	2019-02-02
发表期刊	SENSORS
ISSN	1424-8220
卷号	19 期号:4 页码:23
摘要	Accurate stride-length estimation is a fundamental component in numerous applications, such as pedestrian dead reckoning, gait analysis, and human activity recognition. The existing stride-length estimation algorithms work relatively well in cases of walking a straight line at normal speed, but their error overgrows in complex scenes. Inaccurate walking-distance estimation leads to huge accumulative positioning errors of pedestrian dead reckoning. This paper proposes TapeLine, an adaptive stride-length estimation algorithm that automatically estimates a pedestrian's stride-length and walking-distance using the low-cost inertial-sensor embedded in a smartphone. TapeLine consists of a Long Short-Term Memory module and Denoising Autoencoders that aim to sanitize the noise in raw inertial-sensor data. In addition to accelerometer and gyroscope readings during stride interval, extracted higher-level features based on excellent early studies were also fed to proposed network model for stride-length estimation. To train the model and evaluate its performance, we designed a platform to collect inertial-sensor measurements from a smartphone as training data, pedestrian step events, actual stride-length, and cumulative walking-distance from a foot-mounted inertial navigation system module as training labels at the same time. We conducted elaborate experiments to verify the performance of the proposed algorithm and compared it with the state-of-the-art SLE algorithms. The experimental results demonstrated that the proposed algorithm outperformed the existing methods and achieves good estimation accuracy, with a stride-length error rate of 4.63% and a walking-distance error rate of 1.43% using inertial-sensor embedded in smartphone without depending on any additional infrastructure or pre-collected database when a pedestrian is walking in both indoor and outdoor complex environments (stairs, spiral stairs, escalators and elevators) with natural motion patterns (fast walking, normal walking, slow walking, running, jumping).
关键词	indoor positioning deep learning pedestrian dead reckoning walking distance stride-length estimation
DOI	10.3390/s19040840
收录类别	SCI
语种	英语
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[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	Chemistry ; Electrochemistry ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation
WOS记录号	WOS:000460829200091
出版者	MDPI
引用统计	被引频次：59[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4134
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; Men, Aidong
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, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, Peoples R China
推荐引用方式 GB/T 7714	Wang, Qu,Ye, Langlang,Luo, Haiyong,et al. Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders[J]. SENSORS,2019,19(4):23.
APA	Wang, Qu,Ye, Langlang,Luo, Haiyong,Men, Aidong,Zhao, Fang,&Huang, Yan.(2019).Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders.SENSORS,19(4),23.
MLA	Wang, Qu,et al."Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders".SENSORS 19.4(2019):23.