Skip navigation Return to standard layout with columns
Menu

Control of Neuralprostheses I: Sensor Fusion

By Andrews, Brian J. Williamson, Richard Oulette, Narce Koles, Andrew Department of Biomedical Engineering, University of Alberta, Edmonton, Canada; Proceedings of the RESNA '96 Annual Conference, Vol. 16
(Pages: 282-284) Publication Date: 1996

This article describes a study whose aim is to discover if machine learning technology can be used to map a suite of sensors to a comprehensive set of biomechanical variables required for control of neuralprotheses. Senors currently being evaluated are: micro-machined accelerometers with a DC response; strain gauges; a custom position and angle sensor. Tests were conducted with various combinations of these devices in subject waistbands and ankle foot orthoses. Graphs show typical sensor signals and anatomical positions, velocities and accelerations.
Published by: Rehabilitation Engineering & Assistive Technology Society of North America (RESNA)   (Website:http://www.resna.org)
Link to text: http://www.resna.org
  • Home
  • Alternate Layout
  • My AbleData
  • Contact
  • Site Map
  • A-Z Index
  • Help
  • Privacy

    • AbleData, 8630 Fenton Street, Suite 930, Silver Spring, MD 20910. 1-800-227-0216.
    Maintained for the National Institute on Disability and Rehabilitation Research of the U.S. Dept. of Education
    by ICF Macro under Contract No. ED-04-CO-0018/0007.

    The records in AbleData are provided for information purposes only. Neither the U.S. Department of Education nor ICF Macro has examined, reviewed, or tested any product, device, or information contained in AbleData. The Department and ICF Macro make no endorsement, representation, or warranty express or implied as to any product, device, or information set forth in AbleData. The views expressed on this site do not necessarily represent the opinions of the Department of Education, the National Institute on Disability and Rehabilitation Research, or ICF Macro.