RIC's novel Body Machine Interface (BMI) turns the body into a joystick and promotes motor rehabilitation. The question we aim to answer in this project is whether this BMI interface can be used to control a complex assistive robot ?
Our proposed solution is to leverage robotics autonomy to handle a portion of the control, and gradually transfer more and more control to the human operator. To train the operator to generate higher and higher dimensional control signals, we additionally leverage machine learning in deciding when to unlock additional control dimensions for human operation.
In collaboration with Ferdinando Mussa-Ivaldi, Biomedical Engineering, Northwestern University and the Rehabilitation Institute of Chicago.
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