Analysis of biomechanical data to determine the degree of users participation during robotic-assisted gait rehabilitation

I. Collantes*, G. Asin, J. C. Moreno, Jose L Pons

*Corresponding author for this work

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

6 Scopus citations

Abstract

Recent studies have shown evidence indicating that effective robotic rehabilitation is only possible when the user actively participates during training. Providing a complete effective biofeedback to the patient representing his compliance to the therapy and his performance is thought that his active participation will be enhanced significantly, thus, improving his rehabilitation. We have performed a study with the driven gait orthosis (DGO) Lokomat (Hocoma AG, Volketswil, Switzerland). The objective of the present study is the analysis of the effect of different types of participation (attention to the functional task) from subjects receiving robotic assisted gait training on the kinematic and kinetic patterns. The obtained results provide useful evidence of specific biomechanical features that can be used to design more useful, robust, focused and intuitive biomechanical biofeedback during robotic assisted gait rehabilitation in stroke survivors.

Original languageEnglish (US)
Title of host publication2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
Pages4855-4858
Number of pages4
DOIs
StatePublished - Dec 14 2012
Event34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States
Duration: Aug 28 2012Sep 1 2012

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
CountryUnited States
CitySan Diego, CA
Period8/28/129/1/12

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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