Semi-autonomous robot-assisted cooperative therapy exercises for a therapist's interaction with a patient

Carlos Manuel Martinez, Jason Fong, S. Farokh Atashzar, Mahdi Tavakoli

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

Abstract

Recent increases in demand for post-stroke motor rehabilitation services together with limited time of therapist and accessibility issues, in particular for patients living in remote areas, have created a significant burden on healthcare systems worldwide. Semi-autonomous techniques that allow for sharing the time of a therapist between multiple patients have attracted great interest. Among them Learning from Demonstration (LfD) based robots have been studied as solutions to address this growing demand. In this work, a Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) based LfD approach are proposed to generate a versatile framework to deliver rehabilitation in the absence of the therapist. To collect data for training the models, a bilateral telerehabilitation system is used to enable patient-therapist collaborative task performance is one Degree of Freedom (DOF). The performance and generalizability of the trained model are demonstrated for a variety of patient actions.

Original languageEnglish (US)
Title of host publicationGlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728127231
DOIs
StatePublished - Nov 2019
Event7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019 - Ottawa, Canada
Duration: Nov 11 2019Nov 14 2019

Publication series

NameGlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings

Conference

Conference7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019
CountryCanada
CityOttawa
Period11/11/1911/14/19

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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