Stride detection for pedestrian trajectory reconstruction: a machine learning approach based on geometric patterns

Frédéric Chazal 1 Bertrand Beaufils 2, 1 Marc Grelet 2 Bertrand Michel 3, 1, 4
1 DATASHAPE - Understanding the Shape of Data
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
2 SYSNAV
3 ECN - École Centrale de Nantes
4 LMJL - Laboratoire de Mathématiques Jean Leray
Abstract : —In this paper, a strides detection algorithm is proposed using inertial sensors worn on the ankle. This innovative approach based on geometric patterns can detect both normal walking strides and atypical strides such as small steps, side steps and backward walking that existing methods struggle to detect. It is also robust in critical situations, when for example the wearer is sitting and moving the ankle, while most algorithms in the literature would wrongly detect strides.
Type de document :
Communication dans un congrès
IPIN 2017 - 8th International Conference on Indoor Positioning and Indoor Navigation, Sep 2017, Sapporo, Japan. IEEE, Indoor Positioning and Indoor Navigation (IPIN), 2017 International Conference on, pp.1-6, 〈http://www.ipin2017.org/〉. 〈10.1109/IPIN.2017.8115867〉
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Contributeur : Frédéric Chazal <>
Soumis le : mardi 19 décembre 2017 - 18:54:17
Dernière modification le : mercredi 12 septembre 2018 - 01:15:40

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Frédéric Chazal, Bertrand Beaufils, Marc Grelet, Bertrand Michel. Stride detection for pedestrian trajectory reconstruction: a machine learning approach based on geometric patterns. IPIN 2017 - 8th International Conference on Indoor Positioning and Indoor Navigation, Sep 2017, Sapporo, Japan. IEEE, Indoor Positioning and Indoor Navigation (IPIN), 2017 International Conference on, pp.1-6, 〈http://www.ipin2017.org/〉. 〈10.1109/IPIN.2017.8115867〉. 〈hal-01664659〉

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