http://www.jneuroengrehab.com/content/10/1/75/abstract
Abstract (provisional)
Background
Several studies investigating the use of electromyographic (EMG) signals in robot-based
stroke neuro-rehabilitation to enhance functional recovery. Here we explored whether
a classical EMG-based patterns recognition approach could be employed to predict patients'
intentions while attempting to generate goal-directed movements in the horizontal
plane.
Methods
Nine right-handed healthy subjects and seven right-handed stroke survivors performed
reaching movements in the horizontal plane. EMG signals were recorded and used to
identify the intended motion direction of the subjects. To this aim, a standard pattern
recognition algorithm (i.e., Support Vector Machine, SVM) was used. Different tests
were carried out to understand the role of the inter- and intra-subjects' variability
in affecting classifier accuracy. Abnormal muscular spatial patterns generating misclassification
were evaluated by means of an assessment index calculated from the results achieved
with the PCA, i.e., the so-called Coefficient of Expressiveness (CoE).
Results
Processing the EMG signals of the healthy subjects, in most of the cases we were able
to build a static functional map of the EMG activation patterns for point-to-point
reaching movements on the horizontal plane. On the contrary, when processing the EMG
signals of the pathological subjects a good classification was not possible. In particular,
patients' aimed movement direction was not predictable with sufficient accuracy either
when using the general map extracted from data of normal subjects and when tuning
the classifier on the EMG signals recorded from each patient.
Conclusions
The experimental findings herein reported show that the use of EMG patterns recognition
approach might not be practical to decode movement intention in subjects with neurological
injury such as stroke. Rather than estimate motion from EMGs, future scenarios should
encourage the utilization of these signals to detect and interpret the normal and
abnormal muscle patterns and provide feedback on their correct recruitment.
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