MRI-compatible grasping force sensor with an inclined double parallel structure using fiber optics

Jumpei Arata, Shogo Terakawa, Hideo Fujimoto, James Sulzer, Roger Gassert

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Investigations of human motor control using functional magnetic resonance imaging (fMRI) are increasingly receiving attention, with applications in fields such as motor learning and rehabilitation. In these neuroscience studies, force and position sensors are used to control haptic devices and safely interact with the human motion in an MR environment. However, conventional force sensors such as strain gauges are known to cause electromagnetic interference originating from electrical cables, transducers, and electronics. Light transmission through optical fibers is one alternative that avoids these problems. Since optical fibers do not produce electromagnetic noise, they can be used in an MR environment without electromagnetic interference. In this paper, we propose a novel design of an MRI-compatible grasping force sensor based on these principles. The sensor structure was designed to fit into an MRI scanner with its inclined double parallel mechanism, and was specifically adapted to precision grip tasks. This paper presents the sensor design and preliminary characterization in a non-MR environment.

Original languageEnglish
Title of host publicationASME/ISCIE 2012 International Symposium on Flexible Automation, ISFA 2012
PublisherAmerican Society of Mechanical Engineers (ASME)
Pages77-82
Number of pages6
ISBN (Print)9780791845110
DOIs
Publication statusPublished - Jan 1 2012
Externally publishedYes
EventASME/ISCIE 2012 International Symposium on Flexible Automation, ISFA 2012 - St. Louis, MO, United States
Duration: Jun 18 2012Jun 20 2012

Publication series

NameASME/ISCIE 2012 International Symposium on Flexible Automation, ISFA 2012

Other

OtherASME/ISCIE 2012 International Symposium on Flexible Automation, ISFA 2012
CountryUnited States
CitySt. Louis, MO
Period6/18/126/20/12

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering

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