Gait trajectory generation is a crucial point in the lower limb exoskeleton. Personalized gait trajectories can be supplied to exoskeleton for different patients, because of the availability of the gait synergy. In this paper, a gait synergy model between lower limbs is established based on the Long Short-Term Memory (LSTM). The gait synergy model predicts the values of each joint on the affected side according to the angles and angular velocities of each joint on the healthy side. Three indexes are adopted to evaluate the prediction results with the R2 coefficient being more appropriate for the synergy model than the Main Absolute Percentage Error (MAPE) and Root Mean Square Error(RMSE). Experimental findings reveal that the lower limb gait synergy model based on LSTM is capable of predicting results for hip and knee joints accurately.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Gait Synergy Modeling and Joint Angle Prediction Based on LSTM


    Beteiligte:
    Huang, Yilong (Autor:in) / Yang, Lingling (Autor:in) / Lin, Zeqiang (Autor:in)


    Erscheinungsdatum :

    2023-08-11


    Format / Umfang :

    4827169 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Gait Trajectory and Gait Phase Prediction Based on an LSTM Network

    Su, Binbin / Gutierrez-Farewik, Elena | BASE | 2020

    Freier Zugriff

    Abnormal Gait Recognition Using 3D Joint information of Multiple Kinects System and RNN-LSTM

    Lee, Deok-Won / Jun, Kooksung / Lee, Sanghyub et al. | IEEE | 2019


    Efficiency of deep neural networks for joint angle modeling in digital gait assessment

    Conte Alcaraz, Javier / Moghaddamnia, Sanam / Peissig, Jürgen | BASE | 2021

    Freier Zugriff

    Efficiency of deep neural networks for joint angle modeling in digital gait assessment

    Alcaraz, Javier Conte / Moghaddamnia, Sanam / Peissig, Jürgen | BASE | 2021

    Freier Zugriff

    LSTM LSTM-based future threat prediction method and apparatus

    PARK YOUNG TACK / JEON MYUNG JOONG / KIM MIN SUNG et al. | Europäisches Patentamt | 2021

    Freier Zugriff