Methodology
Exploring the bases for a mixed reality stroke rehabilitation system, Part II: Design of Interactive Feedback for upper limb rehabilitation
Nicole Lehrer1*, Yinpeng Chen1, Margaret Duff1,2, Steven L Wolf1,3 and Thanassis Rikakis1
Author Affiliations
1 School of Arts, Media and Engineering, Arizona State University, Tempe, USA
2 Department of Bioengineering, Arizona State University, Tempe, USA
3 Department of Rehabilitation Medicine, Emory University, Atlanta, USA
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Journal of NeuroEngineering and Rehabilitation 2011, 8:54 doi:10.1186/1743-0003-8-54
Published: 8 September 2011
Abstract
Background
Few existing interactive rehabilitation systems can effectively communicate multiple
aspects of movement performance simultaneously, in a manner that appropriately adapts
across various training scenarios. In order to address the need for such systems within
stroke rehabilitation training, a unified approach for designing interactive systems
for upper limb rehabilitation of stroke survivors has been developed and applied for
the implementation of an Adaptive Mixed Reality Rehabilitation (AMRR) System.
Results
The AMRR system provides computational evaluation and multimedia feedback for the
upper limb rehabilitation of stroke survivors. A participant's movements are tracked
by motion capture technology and evaluated by computational means. The resulting data
are used to generate interactive media-based feedback that communicates to the participant
detailed, intuitive evaluations of his performance. This article describes how the
AMRR system's interactive feedback is designed to address specific movement challenges
faced by stroke survivors. Multimedia examples are provided to illustrate each feedback
component. Supportive data are provided for three participants of varying impairment
levels to demonstrate the system's ability to train both targeted and integrated aspects
of movement.
Conclusions
The AMRR system supports training of multiple movement aspects together or in isolation,
within adaptable sequences, through cohesive feedback that is based on formalized
compositional design principles. From preliminary analysis of the data, we infer that
the system's ability to train multiple foci together or in isolation in adaptable
sequences, utilizing appropriately designed feedback, can lead to functional improvement.
The evaluation and feedback frameworks established within the AMRR system will be
applied to the development of a novel home-based system to provide an engaging yet
low-cost extension of training for longer periods of time.