Upper limb recovery prediction after stroke rehabilitation based on regression method

Ghada M. Bani Musa, Fady Alnajjar, Adel Al-Jumaily, Shingo Shimoda

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)


In this paper, we investigate the possibility of a machine-learning algorithm using the Support Victor Machine Regression (SVMR) to predict the motor functional recovery of moderate post stroke patients during their rehabilitation program. To train the model, we used the recorded electromyography (EMG) signals from the upper limb muscles of the patients during their initial rehabilitation sessions. Then we tested the trained model to predict the later muscles performance of the patient during the same sessions. The results of this pilot study were promising; data were, to some extent, predictable. We believe such research direction could be essential to motivate the patient to complete the designed rehabilitation program and can assist the therapist to innovate proper rehabilitation menu for individual patients.

Original languageEnglish
Title of host publicationBiosystems and Biorobotics
PublisherSpringer International Publishing
Number of pages5
Publication statusPublished - 2019

Publication series

NameBiosystems and Biorobotics
ISSN (Print)2195-3562
ISSN (Electronic)2195-3570


  • Regression
  • Rehabilitation
  • SVMR
  • Upper limb

ASJC Scopus subject areas

  • Biomedical Engineering
  • Mechanical Engineering
  • Artificial Intelligence


Dive into the research topics of 'Upper limb recovery prediction after stroke rehabilitation based on regression method'. Together they form a unique fingerprint.

Cite this