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Medical & Biological Engineering & Computing
Erschienen in: Medical & Biological Engineering & Computing 10/2011

01.10.2011 | Special Issue - Review

Assessing the effectiveness of robot facilitated neurorehabilitation for relearning motor skills following a stroke

verfasst von: W. S. Harwin, A. Murgia, E. K. Stokes

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 10/2011

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Abstract

A growing awareness of the potential for machine-mediated neurorehabilitation has led to several novel concepts for delivering these therapies. To get from laboratory demonstrators and prototypes to the point where the concepts can be used by clinicians in practice still requires significant additional effort, not least in the requirement to assess and measure the impact of any proposed solution. To be widely accepted a study is required to use validated clinical measures but these tend to be subjective, costly to administer and may be insensitive to the effect of the treatment. Although this situation will not change, there is good reason to consider both clinical and mechanical assessments of recovery. This article outlines the problems in measuring the impact of an intervention and explores the concept of providing more mechanical assessment techniques and ultimately the possibility of combining the assessment process with aspects of the intervention.
Vorheriger Artikel Trends in rehabilitation robotics
Nächster Artikel Advances in upper limb stroke rehabilitation: a technology push

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Fußnoten
1
Hocoma, Switzerland.
 
2
A Hammerstein model is a simple non-linear model that consists of a static non-linear element that shapes the input variable, followed by a linear dynamic element.
 
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Metadaten
Titel
Assessing the effectiveness of robot facilitated neurorehabilitation for relearning motor skills following a stroke
verfasst von
W. S. Harwin
A. Murgia
E. K. Stokes
Publikationsdatum
01.10.2011
Verlag
Springer-Verlag
Erschienen in
Medical & Biological Engineering & Computing / Ausgabe 10/2011
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI
https://doi.org/10.1007/s11517-011-0799-y

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