The analysis of human gait is an important tool in medicine and rehabilitation to evaluate the effects and the progression of neurological diseases resulting in neuromotor disorders. In these fields, the gold standard techniques adopted to perform gait analysis rely on motion capture systems and markers. However, these systems present drawbacks: they are expensive, time consuming and they can affect the naturalness of the motion. For these reasons, in the last few years, considerable effort has been spent to study and implement markerless systems based on videography for gait analysis. Unfortunately, only few studies quantitatively compare the differences between markerless and marker-based systems in 3D settings. This work presented a new RGB video-based markerless system leveraging computer vision and deep learning to perform 3D gait analysis. These results were compared with those obtained by a marker-based motion capture system. To this end, we acquired simultaneously with the two systems a multimodal dataset of 16 people repeatedly walking in an indoor environment. With the two methods we obtained similar spatio-temporal parameters. The joint angles were comparable, except for a slight underestimation of the maximum flexion for ankle and knee angles. Taking together these results highlighted the possibility to adopt markerless technique for gait analysis.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Markerless vs. Marker-Based Gait Analysis: A Proof of Concept Study


    Beteiligte:

    Erscheinungsdatum :

    2022-01-01



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Markerless gait analysis vision system for real-time gait monitoring

    André, João Carlos Vieira Peixoto / Lopes, João Pedro Mendes / Palermo, Manuel Castro et al. | BASE | 2020

    Freier Zugriff

    On the reliability of single-camera markerless systems for overground gait monitoring

    Boldo, Michele / Di Marco, Roberto / Martini, Enrico et al. | BASE | 2024

    Freier Zugriff

    Modeling and Simulation of Walking with a Mobile Gait Rehabilitation System Using Markerless Motion Data

    Slavnić, S. / Leu, A. / Ristić-Durrant, D. et al. | Springer Verlag | 2013


    Intelligent Gait Analysis using Marker Based Motion Capturing System

    Möbius, Danny / Kaden, Marika / Staps, Daniel et al. | BASE | 2021

    Freier Zugriff

    Mocap gait motion samples - Optical marker tajectories

    Jiménez Bascones, Juan Luis / Graña Romay, Manuel | BASE | 2018

    Freier Zugriff