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Conference Papers Year : 2019

Robust pedestrian trajectory reconstruction from inertial sensor

Bertrand Beaufils
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Frédéric Chazal
Marc Grelet
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Bertrand Michel

Abstract

In this paper, a strides detection algorithm combined with a technique inspired by Zero Velocity Update (ZUPT) is proposed using inertial sensors worn on the ankle. This innovative approach based on a sensors alignment and machine learning can detect both normal walking strides and atypical strides such as small steps, side steps and backward walking that existing methods struggle to detect. As a consequence, the trajectory reconstruction achieves better performances in daily life contexts for example, where a lot of these kinds of strides are performed in narrow areas such as in a house. It is also robust in critical situations, when for example the wearer is sitting and moving the ankle or bicycling, while most algorithms in the literature would wrongly detect strides and produce error in the trajectory reconstruction by generating movements. Our algorithm is evaluated on more than 7800 strides from seven different subjects performing several activities. We validated the trajectory reconstruction during motion capture sessions by analyzing the stride length. Finally, we tested the algorithm in a challenging situation by plotting the computed trajectory on the building map of an 5 hours and 30 minutes office worker recording.
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Dates and versions

hal-02271580 , version 1 (15-11-2019)

Identifiers

  • HAL Id : hal-02271580 , version 1

Cite

Bertrand Beaufils, Frédéric Chazal, Marc Grelet, Bertrand Michel. Robust pedestrian trajectory reconstruction from inertial sensor. IPIN 2019 - 10th International Conference on Indoor Positioning and Indoor Navigation, Sep 2019, Pisa, Italy. ⟨hal-02271580⟩
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