Strokes or injuries may cause lower-limb severe movement deficits. These years, lower limb rehabilitation robots show great potential in rehabilitation. In the exoskeleton control strategy, the gait prediction or gait planning becomes a fundamental problem. This paper focuses on the gait prediction using Gated Recurrent Unit Model, working on the raw data captured from an optical motion capture platform by a healthy subject. The designed model learns the history gait data and predicts the current values based on the axis values from the hip and the ankle, without any manual feature engineering. The univariate axis values are processed to a 3D shape to the supervised prediction model. Experimental results show the excellent prediction performance in terms of the error rate of 0.0 or 0.01 on all the 31 trails. The accurately predicted values can be used as the reference trajectories for later rehabilitation. Furthermore, the predicted axis values can be further transformed into the torque values or the angular velocity values used as the actuation input for an exoskeleton robot.


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    Titel :

    Human Gait Prediction for Lower Limb Rehabilitation Exoskeleton Using Gated Recurrent Units


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Chew, Esyin (Herausgeber:in) / P. P. Abdul Majeed, Anwar (Herausgeber:in) / Liu, Pengcheng (Herausgeber:in) / Platts, Jon (Herausgeber:in) / Myung, Hyun (Herausgeber:in) / Kim, Junmo (Herausgeber:in) / Kim, Jong-Hwan (Herausgeber:in) / Wang, Yan (Autor:in) / Li, Zhikang (Autor:in) / Chen, Yuguo (Autor:in)

    Erschienen in:

    RiTA 2020 ; Kapitel : 15 ; 128-135


    Erscheinungsdatum :

    2021-08-05


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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