Imperial College London > Talks@ee.imperial > Control and Power Seminars > Iterative Learning Control for Robotic based Stroke Rehabilitation

Iterative Learning Control for Robotic based Stroke Rehabilitation

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If you have a question about this talk, please contact Eric C Kerrigan.

An inability to perform tasks involving reaching is a common problem following stroke. Evidence supports the use of robotic therapy and functional electrical stimulation (FES) to reduce upper limb impairments, but current systems may not encourage maximal voluntary contribution from the participant because assistance is not responsive to performance.

The first part of this seminar will describe the use of iterative learning control schemes to apply functional electrical stimulation to the triceps of subjects in order to perform trajectory tracking tasks in the 2D plane, such as reaching out to an object. The subjects supply voluntary effort and a robotic workstation is used to constrain their movement, impose known dynamics at the point of interaction with the robot, and provide assistive torque about the shoulder as required. Results from clinical trials which highlight the potential of this approach will also be given. In the second part of the seminar, ongoing research on the extension of this approach to the 3D plane will be described where a basic task here is, for example, to reach out and then lift the arm. Results in this part will focus on modeling followed by iterative learning control law design based on input/output linearisation and Newton based methods. Finally, some areas for further research will be discussed.

This talk is part of the Control and Power Seminars series.

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