Explicit forward gait prediction using parametric trajectories adaptation



Thomas Bonis Nicolas Pronost Gilmar F. Santos Christof Hurschler Saida Bouakaz
Université de Lyon, Université Lyon 1 Université de Lyon, Université Lyon 1 Department of Orthopedics, Hannover Medical School Department of Orthopedics, Hannover Medical School Université de Lyon, Université Lyon 1
LIRIS CNRS UMR5205 LIRIS CNRS UMR5205 Laboratory for Biomechanics and Biomaterials Laboratory for Biomechanics and Biomaterials LIRIS CNRS UMR5205





Abstract :
Performing a subject specific and accurate predictive numerical gait simulation can be of great help in many clinical tasks. Though predictive methods often take into account the modifications applied to a reference motion, they are not always able to include the characteristics and the stability of the predicted motion. We propose an optimization-based approach that includes the resulting characteristics of the predicted motion. The optimization is enhanced by the use of parametric curves to represent the motion trajectories. Experimental studies on subjects with different gait patterns confirmed that our method preserves the characteristics of the gait.

Paper :
Paper presented at Journées Françaises de l'Informatique Graphique (J.FIG), Sophia Antipolis (2021). Download the paper