Human gait is an important biometric feature for automatic people recognition. Biometric methodologies are generally intrusive and require the collaboration of the subject in order to perform accurate data acquisition. Gait, instead, can be captured at a distance and without collaboration. This makes it an unobtrusive method for recognizing people in video surveillance systems. In this paper we propose a method to characterize walking gait using three-dimensional skeleton information acquired by the Microsoft Kinect sensor. A set of static and dynamic features correlated to human gait are extracted by the estimated skeleton joint positions. Moreover, we proposed to describe joints positions in a coordinate reference system oriented according to the walking direction to better represents the movement of human body. Using unsupervised clustering over a set of 20 subjects we analyze the effectiveness of the selected features in discriminating people gaits. It turns out that a few dynamic parameters involving the movement of knees, elbows and head are good candidates for robust gait characterization.


    Access

    Download


    Export, share and cite



    Title :

    Gait characterization using dynamic skeleton acquisition


    Contributors:
    E. Gianaria (author) / N. Balossino (author) / M. Grangetto (author) / M. Lucenteforte (author)

    Publication date :

    2013-01-01



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629



    Robust gait identification using Kinect dynamic skeleton data

    Gianaria, Elena / Grangetto, Marco | BASE | 2019

    Free access


    Teaching skeleton robot and motion data acquisition modeling system

    SUI WEIQIANG | European Patent Office | 2020

    Free access

    Characterization of Human Gait using Fuzzy Logic

    Kutilek, Patrik / Viteckova, Slavka / Svoboda, Zdenek | BASE | 2013

    Free access

    Stable Dynamic Walking Gait Humanoid

    Daut, Mohamad Noraffandi / Azyze, Nur Latif / Sanhoury, Ibrahim M. H. et al. | Springer Verlag | 2013