Literature DB >> 19184156

Improving backdrivability in geared rehabilitation robots.

Tobias Nef1, Peter Lum.   

Abstract

Many rehabilitation robots use electric motors with gears. The backdrivability of geared drives is poor due to friction. While it is common practice to use velocity measurements to compensate for kinetic friction, breakaway friction usually cannot be compensated for without the use of an additional force sensor that directly measures the interaction force between the human and the robot. Therefore, in robots without force sensors, subjects must overcome a large breakaway torque to initiate user-driven movements, which are important for motor learning. In this technical note, a new methodology to compensate for both kinetic and breakaway friction is presented. The basic strategy is to take advantage of the fact that, for rehabilitation exercises, the direction of the desired motion is often known. By applying the new method to three implementation examples, including drives with gear reduction ratios 100-435, the peak breakaway torque could be reduced by 60-80%.

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Year:  2009        PMID: 19184156     DOI: 10.1007/s11517-009-0437-0

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  5 in total

1.  Motor learning elicited by voluntary drive.

Authors:  Martin Lotze; Christoph Braun; Niels Birbaumer; Silke Anders; Leonardo G Cohen
Journal:  Brain       Date:  2003-04       Impact factor: 13.501

2.  ARMin: a robot for patient-cooperative arm therapy.

Authors:  Tobias Nef; Matjaz Mihelj; Robert Riener
Journal:  Med Biol Eng Comput       Date:  2007-08-03       Impact factor: 2.602

3.  Neural-network approximation of piecewise continuous functions: application to friction compensation.

Authors:  R R Selmic; F L Lewis
Journal:  IEEE Trans Neural Netw       Date:  2002

Review 4.  Activity-based therapies.

Authors:  Alexander W Dromerick; Peter S Lum; Joseph Hidler
Journal:  NeuroRx       Date:  2006-10

5.  Rehabilitation robotics: pilot trial of a spatial extension for MIT-Manus.

Authors:  Hermano I Krebs; Mark Ferraro; Stephen P Buerger; Miranda J Newbery; Antonio Makiyama; Michael Sandmann; Daniel Lynch; Bruce T Volpe; Neville Hogan
Journal:  J Neuroeng Rehabil       Date:  2004-10-26       Impact factor: 4.262

  5 in total
  5 in total

1.  Oscillator-based assistance of cyclical movements: model-based and model-free approaches.

Authors:  Renaud Ronsse; Tommaso Lenzi; Nicola Vitiello; Bram Koopman; Edwin van Asseldonk; Stefano Marco Maria De Rossi; Jesse van den Kieboom; Herman van der Kooij; Maria Chiara Carrozza; Auke Jan Ijspeert
Journal:  Med Biol Eng Comput       Date:  2011-09-01       Impact factor: 2.602

2.  A robotic system to train activities of daily living in a virtual environment.

Authors:  Marco Guidali; Alexander Duschau-Wicke; Simon Broggi; Verena Klamroth-Marganska; Tobias Nef; Robert Riener
Journal:  Med Biol Eng Comput       Date:  2011-07-28       Impact factor: 2.602

3.  Development and pilot testing of HEXORR: hand EXOskeleton rehabilitation robot.

Authors:  Christopher N Schabowsky; Sasha B Godfrey; Rahsaan J Holley; Peter S Lum
Journal:  J Neuroeng Rehabil       Date:  2010-07-28       Impact factor: 4.262

4.  Assessing User Transparency with Muscle Synergies during Exoskeleton-Assisted Movements: A Pilot Study on the LIGHTarm Device for Neurorehabilitation.

Authors:  Andrea Chiavenna; Alessandro Scano; Matteo Malosio; Lorenzo Molinari Tosatti; Franco Molteni
Journal:  Appl Bionics Biomech       Date:  2018-06-03       Impact factor: 1.781

Review 5.  Review on Patient-Cooperative Control Strategies for Upper-Limb Rehabilitation Exoskeletons.

Authors:  Stefano Dalla Gasperina; Loris Roveda; Alessandra Pedrocchi; Francesco Braghin; Marta Gandolla
Journal:  Front Robot AI       Date:  2021-12-07
  5 in total

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