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Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders

文献类型：期刊论文

作者Huang, Yan1; Zhao, Fang2; Men, Aidong4; Luo, Haiyong3; Ye, Langlang3; Wang, Qu4
刊名SENSORS
出版日期2019-02-02
卷号19期号:4页码:23
关键词indoor positioning deep learning pedestrian dead reckoning walking distance stride-length estimation
ISSN号1424-8220
DOI10.3390/s19040840
英文摘要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).
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
语种英语
WOS记录号WOS:000460829200091
出版者MDPI
源URL[http://119.78.100.204/handle/2XEOYT63/4133]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Men, Aidong; Luo, Haiyong
作者单位1.Peking Univ, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, Peoples R China
2.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China
3.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, 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		 Huang, Yan,Zhao, Fang,Men, Aidong,et al. Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders[J]. SENSORS,2019,19(4):23.
APA Huang, Yan,Zhao, Fang,Men, Aidong,Luo, Haiyong,Ye, Langlang,&Wang, Qu.(2019).Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders.SENSORS,19(4),23.
MLA Huang, Yan,et al."Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders".SENSORS 19.4(2019):23.

入库方式： OAI收割

来源：计算技术研究所

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