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Open AccessArticle

Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders

1
School of Information and Communication Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China
2
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology Chinese Academy of Sciences, Beijing 100190, China
3
School of Software Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China
4
State Key Laboratory of Advanced Optical Communication Systems and Networks, Peking University, Beijing 100871, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(4), 840; https://doi.org/10.3390/s19040840
Received: 15 December 2018 / Revised: 2 February 2019 / Accepted: 15 February 2019 / Published: 18 February 2019
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). View Full-Text
Keywords: indoor positioning; deep learning; pedestrian dead reckoning; walking distance; stride-length estimation indoor positioning; deep learning; pedestrian dead reckoning; walking distance; stride-length estimation
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MDPI and ACS Style

Wang, Q.; Ye, L.; Luo, H.; Men, A.; Zhao, F.; Huang, Y. Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders. Sensors 2019, 19, 840. https://doi.org/10.3390/s19040840

AMA Style

Wang Q, Ye L, Luo H, Men A, Zhao F, Huang Y. Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders. Sensors. 2019; 19(4):840. https://doi.org/10.3390/s19040840

Chicago/Turabian Style

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, no. 4: 840. https://doi.org/10.3390/s19040840

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