Literature DB >> 16200755

Robotic techniques for upper limb evaluation and rehabilitation of stroke patients.

Roberto Colombo1, Fabrizio Pisano, Silvestro Micera, Alessandra Mazzone, Carmen Delconte, M Chiara Carrozza, Paolo Dario, Giuseppe Minuco.   

Abstract

This paper presents two robot devices for use in the rehabilitation of upper limb movements and reports the quantitative parameters obtained to characterize the rate of improvement, thus allowing a precise monitoring of patient's recovery. A one degree of freedom (DoF) wrist manipulator and a two-DoF elbow-shoulder manipulator were designed using an admittance control strategy; if the patient could not move the handle, the devices completed the motor task. Two groups of chronic post-stroke patients (G1 n = 7, and G2 n = 9) were enrolled in a three week rehabilitation program including standard physical therapy (45 min daily) plus treatment by means of robot devices, respectively, for wrist and elbow-shoulder movements (40 min, twice daily). Both groups were evaluated by means of standard clinical assessment scales and a new robot measured evaluation metrics that included an active movement index quantifying the patient's ability to execute the assigned motor task without robot assistance, the mean velocity, and a movement accuracy index measuring the distance of the executed path from the theoretic one. After treatment, both groups improved their motor deficit and disability. In G1, there was a significant change in the clinical scale values (p < 0.05) and range of motion wrist extension (p < 0.02). G2 showed a significant change in clinical scales (p < 0.01), in strength (p < 0.05) and in the robot measured parameters (p < 0.01). The relationship between robot measured parameters and the clinical assessment scales showed a moderate and significant correlation (r > 0.53 p < 0.03). Our findings suggest that robot-aided neurorehabilitation may improve the motor outcome and disability of chronic post-stroke patients. The new robot measured parameters may provide useful information about the course of treatment and its effectiveness at discharge.

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Year:  2005        PMID: 16200755     DOI: 10.1109/TNSRE.2005.848352

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  59 in total

1.  Minimally assistive robot training for proprioception enhancement.

Authors:  Maura Casadio; Pietro Morasso; Vittorio Sanguineti; Psiche Giannoni
Journal:  Exp Brain Res       Date:  2009-01-13       Impact factor: 1.972

2.  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

3.  Arm-eye coordination test to objectively quantify motor performance and muscles activation in persons after stroke undergoing robot-aided rehabilitation training: a pilot study.

Authors:  Rong Song; Kai-Yu Tong; Xiaoling Hu; Le Li; Rui Sun
Journal:  Exp Brain Res       Date:  2013-02-01       Impact factor: 1.972

4.  Normalized movement quality measures for therapeutic robots strongly correlate with clinical motor impairment measures.

Authors:  Ozkan Celik; Marcia K O'Malley; Corwin Boake; Harvey S Levin; Nuray Yozbatiran; Timothy A Reistetter
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2010-04-12       Impact factor: 3.802

5.  Age is negatively associated with upper limb recovery after conventional but not robotic rehabilitation in patients with stroke: a secondary analysis of a randomized-controlled trial.

Authors:  Francesca Cecchi; Marco Germanotta; Claudio Macchi; Angelo Montesano; Silvia Galeri; Manuela Diverio; Catiuscia Falsini; Monica Martini; Rita Mosca; Emanuele Langone; Dionysia Papadopoulou; Maria Chiara Carrozza; Irene Aprile
Journal:  J Neurol       Date:  2020-08-25       Impact factor: 4.849

6.  Learning, not adaptation, characterizes stroke motor recovery: evidence from kinematic changes induced by robot-assisted therapy in trained and untrained task in the same workspace.

Authors:  L Dipietro; H I Krebs; B T Volpe; J Stein; C Bever; S T Mernoff; S E Fasoli; N Hogan
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2011-12-16       Impact factor: 3.802

Review 7.  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

8.  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

9.  A method for assessing the arm movement performance: probability tube.

Authors:  Miloš Kostić; Mirjana B Popović; Dejan B Popović
Journal:  Med Biol Eng Comput       Date:  2013-08-07       Impact factor: 2.602

Review 10.  Review of control strategies for robotic movement training after neurologic injury.

Authors:  Laura Marchal-Crespo; David J Reinkensmeyer
Journal:  J Neuroeng Rehabil       Date:  2009-06-16       Impact factor: 4.262

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