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Sensing and Force-Feedback Exoskeleton Robotic (SAFER) Glove Mechanism for Hand Rehabilitation

[+] Author Affiliations
Zhou Ma, Pinhas Ben-Tzvi, Jerome Danoff

George Washington University, Washington, DC

Paper No. DETC2015-46661, pp. V05AT08A036; 8 pages
  • ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 5A: 39th Mechanisms and Robotics Conference
  • Boston, Massachusetts, USA, August 2–5, 2015
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5712-0
  • Copyright © 2015 by ASME


This paper presents the design and application of the SAFER glove in the field of hand rehabilitation. The authors present preliminary results on a new hand grasping rehabilitation learning system that is designed to gather kinematic and force information of the human hand and to playback the motion to assist a user in common hand grasping movements, such as grasping a bottle of water. The fingertip contact forces during grasping have been measured by the SAFER Glove from 12 subjects. The measured fingertip contact forces were modeled with Gaussian Mixture Model (GMM) based on machine learning approach. The learned force distributions were then used to generate fingertip force trajectories with a Gaussian Mixture Regression (GMR) method. To demonstrate the glove’s potential to manipulate the hand, experiments with the glove fitted on a wooden hand to grasp various objects were performed. Instead of defining a grasping force, contact force trajectories were used to control the SAFER Glove to actuate/assist this hand while carrying out a learned grasping task. To prove that the hand can be driven safely by the haptic mechanism, force sensor readings placed between each finger and the mechanism have been plotted. The experimental results show the potential of the proposed system in future hand rehabilitation therapy.

Copyright © 2015 by ASME



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