Literature DB >> 19666344

Measuring changes of movement dynamics during robot-aided neurorehabilitation of stroke patients.

Roberto Colombo1, Irma Sterpi, Alessandra Mazzone, Carmen Delconte, Giuseppe Minuco, Fabrizio Pisano.   

Abstract

The aim of this study was to describe in detail a new method, called normalized force control parameter (nFCP), to measure changes in movement dynamics obtained during robot-aided neurorehabilitation, and to evaluate its ability to estimate the clinical scales. The study was conducted in a group of 18 subjects after chronic stroke who underwent robot therapy of the upper limb. We used two different measures of movement dynamics to assess patients' performance during each session of training: the nFCP and force directional error (FDE), both measuring the directional error of the patient-exerted force applied to the end-effector of the robot device. Both metrics exhibited significant changes over the three-week course of treatment. The comparison between nFCP and FDE slopes showed a significant and high correlation ( r = 0.79; p < 0.001), indicating that the two parameters are closely correlated. The FDE informed on the direction of the force error, while the nFCP showed a better performance in predicting the clinical scale values. Assessment of the time course of recovery showed that nFCP, FDE and the movement smoothness improved quickly at first and then plateaued, while steady gains in mean velocity of movement took place over a longer time course. These data may be helpful to the therapist in developing more effective robot-based therapy protocols.

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Year:  2009        PMID: 19666344     DOI: 10.1109/TNSRE.2009.2028831

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


  16 in total

1.  Spectral analyses of wrist motion in individuals poststroke: the development of a performance measure with promise for unsupervised settings.

Authors:  Eric Wade; Christina Chen; Carolee J Winstein
Journal:  Neurorehabil Neural Repair       Date:  2013-11-08       Impact factor: 3.919

2.  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 3.  Neurophysiology of robot-mediated training and therapy: a perspective for future use in clinical populations.

Authors:  Duncan L Turner; Ander Ramos-Murguialday; Niels Birbaumer; Ulrich Hoffmann; Andreas Luft
Journal:  Front Neurol       Date:  2013-11-13       Impact factor: 4.003

Review 4.  A survey on robotic devices for upper limb rehabilitation.

Authors:  Paweł Maciejasz; Jörg Eschweiler; Kurt Gerlach-Hahn; Arne Jansen-Troy; Steffen Leonhardt
Journal:  J Neuroeng Rehabil       Date:  2014-01-09       Impact factor: 4.262

5.  Upper Limb Rehabilitation Robot Powered by PAMs Cooperates with FES Arrays to Realize Reach-to-Grasp Trainings.

Authors:  Xikai Tu; Hualin Han; Jian Huang; Jian Li; Chen Su; Xiaobo Jiang; Jiping He
Journal:  J Healthc Eng       Date:  2017-06-15       Impact factor: 2.682

Review 6.  Neuroplastic Changes Following Brain Ischemia and their Contribution to Stroke Recovery: Novel Approaches in Neurorehabilitation.

Authors:  Claudia Alia; Cristina Spalletti; Stefano Lai; Alessandro Panarese; Giuseppe Lamola; Federica Bertolucci; Fabio Vallone; Angelo Di Garbo; Carmelo Chisari; Silvestro Micera; Matteo Caleo
Journal:  Front Cell Neurosci       Date:  2017-03-16       Impact factor: 5.505

7.  Robotic Assessment of Upper Limb Function in a Nonhuman Primate Model of Chronic Stroke.

Authors:  Yining Chen; Meredith C Poole; Shelby V Olesovsky; Allen A Champagne; Kathleen A Harrison; Joseph Y Nashed; Nicole S Coverdale; Stephen H Scott; Douglas J Cook
Journal:  Transl Stroke Res       Date:  2021-01-03       Impact factor: 6.829

8.  Robotic Assisted Upper Limb Training Post Stroke: A Randomized Control Trial Using Combinatory Approach Toward Reducing Workforce Demands.

Authors:  Aamani Budhota; Karen S G Chua; Asif Hussain; Simone Kager; Adèle Cherpin; Sara Contu; Deshmukh Vishwanath; Christopher W K Kuah; Chwee Yin Ng; Lester H L Yam; Yong Joo Loh; Deshan Kumar Rajeswaran; Liming Xiang; Etienne Burdet; Domenico Campolo
Journal:  Front Neurol       Date:  2021-06-02       Impact factor: 4.003

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

Review 10.  Training modalities in robot-mediated upper limb rehabilitation in stroke: a framework for classification based on a systematic review.

Authors:  Angelo Basteris; Sharon M Nijenhuis; Arno H A Stienen; Jaap H Buurke; Gerdienke B Prange; Farshid Amirabdollahian
Journal:  J Neuroeng Rehabil       Date:  2014-07-10       Impact factor: 4.262

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