Literature DB >> 19139867

Minimally assistive robot training for proprioception enhancement.

Maura Casadio1, Pietro Morasso, Vittorio Sanguineti, Psiche Giannoni.   

Abstract

In stroke survivors, motor impairment is frequently associated with degraded proprioceptive and/or somatosensory functions. Here we address the question of how to use robots to improve proprioception in these patients. We used an 'assist-as-needed' protocol, in which robot assistance was kept to a minimum and was continuously adjusted during exercise. To specifically train proprioceptive functions, we alternated blocks of trials with and without vision. A total of nine chronic stroke survivors participated in the study, which consisted of a total of ten 1-h exercise sessions. We used a linear mixed-effects statistical model to account for the effects of exercise, vision and the degree of assistance on the overall performance, and to capture both the systematic effects and the individual variations. Although there was not always a complete recovery of autonomous movements, all subjects exhibited an increased amount of voluntary control. Moreover, training with closed eyes appeared to be beneficial for patients with abnormal proprioception. Our results indicate that training by alternating vision and no-vision blocks may improve the ability to use proprioception as well as the ability to integrate it with vision. We suggest that the approach may be useful in the more general case of motor skill acquisition, in which enhancing proprioception may improve the ability to physically interact with the external world.

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Year:  2009        PMID: 19139867     DOI: 10.1007/s00221-008-1680-6

Source DB:  PubMed          Journal:  Exp Brain Res        ISSN: 0014-4819            Impact factor:   1.972


  33 in total

1.  Robot-assisted adaptive training: custom force fields for teaching movement patterns.

Authors:  James L Patton; Ferdinando A Mussa-Ivaldi
Journal:  IEEE Trans Biomed Eng       Date:  2004-04       Impact factor: 4.538

2.  Motor adaptation as a greedy optimization of error and effort.

Authors:  Jeremy L Emken; Raul Benitez; Athanasios Sideris; James E Bobrow; David J Reinkensmeyer
Journal:  J Neurophysiol       Date:  2007-03-28       Impact factor: 2.714

3.  The precision of proprioceptive position sense.

Authors:  R J van Beers; A C Sittig; J J Denier van der Gon
Journal:  Exp Brain Res       Date:  1998-10       Impact factor: 1.972

4.  Rubber hands 'feel' touch that eyes see.

Authors:  M Botvinick; J Cohen
Journal:  Nature       Date:  1998-02-19       Impact factor: 49.962

5.  Sensory loss in stroke patients: effective training of tactile and proprioceptive discrimination.

Authors:  L M Carey; T A Matyas; L E Oke
Journal:  Arch Phys Med Rehabil       Date:  1993-06       Impact factor: 3.966

6.  Random-effects models for longitudinal data.

Authors:  N M Laird; J H Ware
Journal:  Biometrics       Date:  1982-12       Impact factor: 2.571

7.  Hemiparetic stroke impairs anticipatory control of arm movement.

Authors:  Craig D Takahashi; David J Reinkensmeyer
Journal:  Exp Brain Res       Date:  2003-01-30       Impact factor: 1.972

Review 8.  Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review.

Authors:  Gert Kwakkel; Boudewijn J Kollen; Hermano I Krebs
Journal:  Neurorehabil Neural Repair       Date:  2007-09-17       Impact factor: 3.919

9.  Movement smoothness changes during stroke recovery.

Authors:  Brandon Rohrer; Susan Fasoli; Hermano Igo Krebs; Richard Hughes; Bruce Volpe; Walter R Frontera; Joel Stein; Neville Hogan
Journal:  J Neurosci       Date:  2002-09-15       Impact factor: 6.167

10.  A proof of concept study for the integration of robot therapy with physiotherapy in the treatment of stroke patients.

Authors:  Maura Casadio; Psiche Giannoni; Pietro Morasso; Vittorio Sanguineti
Journal:  Clin Rehabil       Date:  2009-03       Impact factor: 3.477

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  20 in total

1.  Passive motion paradigm: an alternative to optimal control.

Authors:  Vishwanathan Mohan; Pietro Morasso
Journal:  Front Neurorobot       Date:  2011-12-27       Impact factor: 2.650

2.  Experimental measure of arm stiffness during single reaching movements with a time-frequency analysis.

Authors:  Davide Piovesan; Alberto Pierobon; Paul DiZio; James R Lackner
Journal:  J Neurophysiol       Date:  2013-08-14       Impact factor: 2.714

3.  Current Trends in Robot-Assisted Upper-Limb Stroke Rehabilitation: Promoting Patient Engagement in Therapy.

Authors:  Amy A Blank; James A French; Ali Utku Pehlivan; Marcia K O'Malley
Journal:  Curr Phys Med Rehabil Rep       Date:  2014-09

4.  Adaptation to constant-magnitude assistive forces: kinematic and neural correlates.

Authors:  Vladimir Novakovic; Vittorio Sanguineti
Journal:  Exp Brain Res       Date:  2011-02-09       Impact factor: 1.972

5.  Comparing Two Computational Mechanisms for Explaining Functional Recovery in Robot-Therapy of Stroke Survivors.

Authors:  Davide Piovesan; Maura Casadio; Ferdinando A Mussa-Ivaldi; Pietro Morasso
Journal:  Proc IEEE RAS EMBS Int Conf Biomed Robot Biomechatron       Date:  2012-06

6.  Motor learning with fading and growing haptic guidance.

Authors:  Herbert Heuer; Jenna Lüttgen
Journal:  Exp Brain Res       Date:  2014-04-16       Impact factor: 1.972

7.  Self-adaptive robot training of stroke survivors for continuous tracking movements.

Authors:  Elena Vergaro; Maura Casadio; Valentina Squeri; Psiche Giannoni; Pietro Morasso; Vittorio Sanguineti
Journal:  J Neuroeng Rehabil       Date:  2010-03-15       Impact factor: 4.262

Review 8.  The effectiveness of proprioceptive training for improving motor function: a systematic review.

Authors:  Joshua E Aman; Naveen Elangovan; I-Ling Yeh; Jürgen Konczak
Journal:  Front Hum Neurosci       Date:  2015-01-28       Impact factor: 3.169

9.  Neuromotor recovery from stroke: computational models at central, functional, and muscle synergy level.

Authors:  Maura Casadio; Irene Tamagnone; Susanna Summa; Vittorio Sanguineti
Journal:  Front Comput Neurosci       Date:  2013-08-22       Impact factor: 2.380

10.  Kinematic and neurophysiological consequences of an assisted-force-feedback brain-machine interface training: a case study.

Authors:  Stefano Silvoni; Marianna Cavinato; Chiara Volpato; Giulia Cisotto; Clara Genna; Michela Agostini; Andrea Turolla; Ander Ramos-Murguialday; Francesco Piccione
Journal:  Front Neurol       Date:  2013-11-07       Impact factor: 4.003

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