Literature DB >> 18490177

Quantitative evaluation of motor functional recovery process in chronic stroke patients during robot-assisted wrist training.

X L Hu1, K Y Tong, R Song, X J Zheng, K H Lui, W W F Leung, S Ng, S S Y Au-Yeung.   

Abstract

This study was to investigate the motor functional recovery process in chronic stroke during robot-assisted wrist training. Fifteen subjects with chronic upper extremity paresis after stroke attended a 20-session wrist tracking training using an interactive rehabilitation robot. Electromyographic (EMG) parameters, i.e., EMG activation levels of four muscles: biceps brachii (BIC), triceps brachii (TRI, lateral head), flexor carpiradialis (FCR), and extensor carpiradialis (ECR) and their co-contraction indexes (CI) were used to monitor the neuromuscular changes during the training course. The EMG activation levels of the FCR (11.1% of decrease from the initial), BIC (17.1% of decrease from the initial), and ECR (29.4% of decrease from the initial) muscles decreased significantly during the training (P<0.05). Such decrease was associated with decreased Modified Ashworth Scores for both the wrist and elbow joints (P<0.05). Significant decrease (P<0.05) was also found in CIs of muscle pairs, BIC&TRI (21% of decrease from the initial), FCR&amp;BIC (11.3% of decrease from the initial), ECR&amp;BIC (49.3% of decrease from the initial). The decreased CIs related to the BIC muscle were mainly caused by the reduction in the BIC EMG activation level, suggesting a better isolation of the wrist movements from the elbow motions. The decreased CI of ECR&amp; FCR in the later training sessions (P<0.05) was due to the reduced co-contraction phase of the antagonist muscle pair in the tracking tasks. Significant improvements (P<0.05) were also found in motor outcomes related to the shoulder/elbow and wrist/hand scores assessed by the Fugl-Meyer assessment before and after the training. According to the evolution of the EMG parameters along the training course, further motor improvements could be obtained by providing more training sessions, since the decreases of the EMG parameters did not reach a steady state before the end of the training. The results in this study provided an objective and quantitative EMG measure to describe the motor recovery process during poststroke robot-assisted wrist for the further understanding on the neuromuscular mechanism associated with the recovery.

Entities:  

Mesh:

Year:  2008        PMID: 18490177     DOI: 10.1016/j.jelekin.2008.04.002

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  19 in total

1.  Training-induced changes in the pattern of triceps to biceps activation during reaching tasks after chronic and severe stroke.

Authors:  Ruth Nancy Barker; Sandra Brauer; Richard Carson
Journal:  Exp Brain Res       Date:  2009-06-06       Impact factor: 1.972

2.  Kinematics and Workspace Analysis of xArm6 Robot for Activities of Daily Living.

Authors:  Elias Munoz; Md Samiul Haque Sunny; Ivan Rulik; Javier D Sanjuan De Caro; Mohammad H Rahman
Journal:  Proc Int Conf Ind Mech Eng Oper Manag       Date:  2021

3.  A novel myoelectric pattern recognition strategy for hand function restoration after incomplete cervical spinal cord injury.

Authors:  Jie Liu; Ping Zhou
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2012-09-27       Impact factor: 3.802

4.  The effect of involuntary motor activity on myoelectric pattern recognition: a case study with chronic stroke patients.

Authors:  Xu Zhang; Yun Li; Xiang Chen; Guanglin Li; William Zev Rymer; Ping Zhou
Journal:  J Neural Eng       Date:  2013-07-17       Impact factor: 5.379

5.  Evidence of neuroplasticity with robotic hand exoskeleton for post-stroke rehabilitation: a randomized controlled trial.

Authors:  Neha Singh; Megha Saini; Nand Kumar; M V Padma Srivastava; Amit Mehndiratta
Journal:  J Neuroeng Rehabil       Date:  2021-05-06       Impact factor: 4.262

Review 6.  Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke.

Authors:  Jan Mehrholz; Marcus Pohl; Thomas Platz; Joachim Kugler; Bernhard Elsner
Journal:  Cochrane Database Syst Rev       Date:  2018-09-03

Review 7.  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

Review 8.  Neural coding for effective rehabilitation.

Authors:  Xiaoling Hu; Yiwen Wang; Ting Zhao; Aysegul Gunduz
Journal:  Biomed Res Int       Date:  2014-09-02       Impact factor: 3.411

9.  A Novel Modified Super-Twisting Control Augmented Feedback Linearization for Wearable Robotic Systems Using Time Delay Estimation.

Authors:  Brahim Brahmi; Ibrahim El Bojairami; Tanvir Ahmed; Asif Al Zubayer Swapnil; Mohammad AssadUzZaman; Inga Wang; Erin McGonigle; Mohammad Habibur Rahman
Journal:  Micromachines (Basel)       Date:  2021-05-21       Impact factor: 2.891

10.  Somatosensory inputs by application of KinesioTaping: effects on spasticity, balance, and gait in chronic spinal cord injury.

Authors:  Federica Tamburella; Giorgio Scivoletto; Marco Molinari
Journal:  Front Hum Neurosci       Date:  2014-05-30       Impact factor: 3.169

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.