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Reinforcement learning neural network (RLNN) based adaptive control of fine hand motion rehabilitation robot

Conference Paper


Abstract


  • Recent neural science research suggests that a

    robotic device can be an effective tool to deliver the repetitive

    movement training that is needed to trigger neuroplasticity

    in the brain following neurologic injuries such as stroke

    and spinal cord injury (SCI). In such scenario, adaptive

    control of the robotic device to provide assistance as needed

    along the intended motion trajectory with exact amount of

    force intensity, though complex, is a more effective approach.

    A critic-actor based reinforcement learning neural network

    (RLNN) control method is explored to provide adaptive control

    during post-stroke fine hand motion rehabilitation training.

    The effectiveness of the method is verified through computer

    simulation and implementation on a hand rehabilitation robotic

    device. Results suggest that the control system can fulfil the

    assist-as-needed (AAN) control with high performance and

    reliability. The method demonstrates potential to encourage

    active participation of the patient in the rehabilitation process

    and to improve the efficiency of the process.

Publication Date


  • 2015

Citation


  • X. Huang, F. Naghdy, H. Du, G. Naghdy & C. Todd, "Reinforcement learning neural network (RLNN) based adaptive control of fine hand motion rehabilitation robot,"^^ in Control Applications (CCA), 2015 IEEE Conference on, 2015, pp. 941-946.

Scopus Eid


  • 2-s2.0-84964319155

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5098

Start Page


  • 941

End Page


  • 946

Place Of Publication


  • http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7320733

Abstract


  • Recent neural science research suggests that a

    robotic device can be an effective tool to deliver the repetitive

    movement training that is needed to trigger neuroplasticity

    in the brain following neurologic injuries such as stroke

    and spinal cord injury (SCI). In such scenario, adaptive

    control of the robotic device to provide assistance as needed

    along the intended motion trajectory with exact amount of

    force intensity, though complex, is a more effective approach.

    A critic-actor based reinforcement learning neural network

    (RLNN) control method is explored to provide adaptive control

    during post-stroke fine hand motion rehabilitation training.

    The effectiveness of the method is verified through computer

    simulation and implementation on a hand rehabilitation robotic

    device. Results suggest that the control system can fulfil the

    assist-as-needed (AAN) control with high performance and

    reliability. The method demonstrates potential to encourage

    active participation of the patient in the rehabilitation process

    and to improve the efficiency of the process.

Publication Date


  • 2015

Citation


  • X. Huang, F. Naghdy, H. Du, G. Naghdy & C. Todd, "Reinforcement learning neural network (RLNN) based adaptive control of fine hand motion rehabilitation robot,"^^ in Control Applications (CCA), 2015 IEEE Conference on, 2015, pp. 941-946.

Scopus Eid


  • 2-s2.0-84964319155

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5098

Start Page


  • 941

End Page


  • 946

Place Of Publication


  • http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7320733