The potential predictors of motor performance outcomes after rehabilitation for patients with stroke

Jiann Der Lee, Tzyh Chyang Chang*, Shih Ting Yang, Chung Hsien Huang, Ching Yi Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The identification of potential predictors for motor outcome after rehabilitation helps underscore the factors that may affect treatment outcomes and target individuals who benefit the most from the therapy. In this study, we addressed and utilized a classifier to identify the potential predictors for motor performance outcome for patients with stroke after rehabilitation. The potential predictors selected and used by different assessments in this study were age, sex, time since stroke, education, neurologic status, and the movement performance of the upper extremity. This study aimed to identify predictors of motor performance outcomes after rehabilitation for stroke patients. The PSO-SVM was chosen in this study to find the predictor of motor function for clients with stroke. The potential predictors for motor outcome after rehabilitation were motor ability assessment of the Fugl-Meyer Assessment (FMA) and the Functional Independence Measure (FIM). This study is to investigate the potential demographic and clinical characteristics of stroke that can serve to predict rehabilitation outcomes in motor performance.

Original languageEnglish
Title of host publicationInnovation for Applied Science and Technology
Pages1656-1660
Number of pages5
DOIs
StatePublished - 2013
Event2nd International Conference on Engineering and Technology Innovation 2012, ICETI 2012 - Kaohsiung, Taiwan
Duration: 02 11 201206 11 2012

Publication series

NameApplied Mechanics and Materials
Volume284-287
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482

Conference

Conference2nd International Conference on Engineering and Technology Innovation 2012, ICETI 2012
Country/TerritoryTaiwan
CityKaohsiung
Period02/11/1206/11/12

Keywords

  • Classification
  • Predictor
  • Stroke

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