Time course of abnormal synergies of stroke patients treated and assessed by a neuro-rehabilitation robot

Pin Cheng Kung, Chou Ching K. Lin, Ming Shaung Ju, Shu Min Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This study aimed at investigating and quantifying the time course of abnormal synergies in the affected upper limbs of chronic stroke patients treated with a neuro-rehabilitation robot. Eight age-matched normal and seven chronic stroke patients were recruited to perform rectilinear tracking movements in four directions for four months. Kinematic, kinetic and electromyographic (EMG) data were used to develop two biomechanical indices and one EMG assessment index from principal component analysis (PCA) to characterize abnormal synergies. Initially, compared with those in normal subjects, the correlation between the elbow angle and forearm pro-supination torque was smaller, the variation of the forearm torque was larger and the abnormal co-contraction of the elbow and shoulder muscles, shown by the coactivation ratio derived from EMGs, was similar for the stroke patients. The correlation increased, the variation decreased and the coactivation ratio decreased with the time course of treatment. In summary, the proposed assessment indices indicated significant improvement of abnormal synergies after four months of treatment with the robot.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Rehabilitation Robotics, ICORR 2009
Pages12-17
Number of pages6
DOIs
Publication statusPublished - 2009 Nov 17
Event2009 IEEE International Conference on Rehabilitation Robotics, ICORR 2009 - Kyoto, Japan
Duration: 2009 Jun 232009 Jun 26

Publication series

Name2009 IEEE International Conference on Rehabilitation Robotics, ICORR 2009

Other

Other2009 IEEE International Conference on Rehabilitation Robotics, ICORR 2009
CountryJapan
CityKyoto
Period09-06-2309-06-26

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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