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Carnegie Mellon Robotics Institute

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Abstract

People with athetoid cerebral palsy (CP) have difficulty using computers due to unintentional involuntary movements in the upper extremities. A neural network-based system has been developed to cancel the undesired motion, and speed up the movements and accuracy in target acquisition and path tracking tasks while using an isometric joystick (IJ). Nonlinear filtering algorithms were created with neural networks using nonlinear models to help people with athetoid CP to access the computer. This paper presents unfiltered test data that have been collected from patients, and describes the planned filtering approach.

Keywords

athetosis, movement disorders, assistive interfaces

Notes

Sponsor: National Institutes of Health

Grant ID: R21HD050717

Associated Center(s) / Consortia: Medical Robotics Technology Center

Associated Lab(s) / Group(s): Surgical Mechatronics Laboratory

Associated Project(s): Joystick Filtering for Movement Disorders

Number of pages: 3

Text Reference

Juan J. V?quez, Kevin C. Olds, Sara Sibenaller, Dan Ding, and Cameron Riviere, "Toward Filtering of Athetoid Motion with Neural Networks," Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, August, 2007, pp. 1434-1436.

BibTeX Reference

@inproceedings{Olds_2007_5988,
   author = "Juan J. V?quez and Kevin C Olds and Sara Sibenaller and Dan Ding and Cameron Riviere",
   title = "Toward Filtering of Athetoid Motion with Neural Networks",
   booktitle = "Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society",
   pages = "1434-1436",
   month = "August",
   year = "2007",
}

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