This paper is in the following e-collection/theme issue:

AI Applications in Biomedical Engineering (8) Robotics in Rehabilitation (38) Machine Learning (1465) Artificial Intelligence (1371) Stroke (163) Cognitive and Neurorehabilitation (262) Neurorehabilitation and Technology (17)

Published on in Vol 9 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56980, first published .
Classifying Residual Stroke Severity Using Robotics-Assisted Stroke Rehabilitation: Machine Learning Approach

Classifying Residual Stroke Severity Using Robotics-Assisted Stroke Rehabilitation: Machine Learning Approach

Classifying Residual Stroke Severity Using Robotics-Assisted Stroke Rehabilitation: Machine Learning Approach

Authors of this article:

Russell Jeter1, 2 Author Orcid Image ;   Raymond Greenfield1 Author Orcid Image ;   Stephen N Housley2, 3 Author Orcid Image ;   Igor Belykh1, 4 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Russell Jeter   1, 2 * , PhD ;   Raymond Greenfield   1 * , MSci ;   Stephen N Housley   2, 3 , PT, DPT, PhD ;   Igor Belykh   1, 4 , Prof Dr, PhD

1 Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, United States

2 Motus Nova, LLC, Atlanta, GA, United States

3 Laboratory for Sensorimotor Integration, Georgia Institute of Technology, Atlanta, GA, United States

4 Neuroscience Institute, Georgia State University, Atlanta, GA, United States

*these authors contributed equally

Corresponding Author:

  • Igor Belykh, Prof Dr, PhD
  • Department of Mathematics and Statistics
  • Georgia State University
  • PO Box 4110
  • Atlanta, GA, 30302 410
  • United States
  • Phone: 1 404-413-6411
  • Fax: 1 404-413-6403
  • Email: ibelykh@gsu.edu