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Journal of Sensors
Volume 2017, Article ID 6091261, 10 pages
Research Article

Pedestrian Stride Length Estimation from IMU Measurements and ANN Based Algorithm

Engineering Research Center for Navigation Technology, Department of Precision Instruments, Tsinghua University, Beijing 100084, China

Correspondence should be addressed to Meifeng Guo; nc.ude.auhgnist.liam@fmoug

Received 21 August 2016; Revised 24 December 2016; Accepted 15 January 2017; Published 9 February 2017

Academic Editor: Andrea Cusano

Copyright © 2017 Haifeng Xing et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Pedestrian dead reckoning (PDR) can be used for continuous position estimation when satellite or other radio signals are not available, and the accuracy of the stride length measurement is important. Current stride length estimation algorithms, including linear and nonlinear models, consider a few variable factors, and some rely on high precision and high cost equipment. This paper puts forward a stride length estimation algorithm based on a back propagation artificial neural network (BP-ANN), using a consumer-grade inertial measurement unit (IMU); it then discusses various factors in the algorithm. The experimental results indicate that the error of the proposed algorithm in estimating the stride length is approximately 2%, which is smaller than that of the frequency and nonlinear models. Compared with the latter two models, the proposed algorithm does not need to determine individual parameters in advance if the trained neural net is effective. It can, thus, be concluded that this algorithm shows superior performance in estimating pedestrian stride length.