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    Can immersive type of Virtual Reality bring EMG pattern changes post facial palsy?

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    Date
    2015
    Author
    Qidwai, Uvais
    Ajimsha, M. S.
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    Abstract
    The loss of facial expression via facial paralysis is a devastating condition, both functionally and aesthetically. However, given the life-long plasticity of the brain one could assume that recovery could be facilitated by the harnessing of mechanisms underlying neuronal reorganization. Currently it is not clear how this reorganization can be mobilized. Novel technology based neurorehabilitation techniques hold promise to address this issue. In this paper an immersive Virtual Reality (VR) based system is presented that is based on a number of hypotheses related to the neural structures targeted for recovery/reorganization, the structure of training system, and the role of individualization. The purpose of this paper is to examine the effects of an immersive type virtual reality (VR) intervention on activation of facial upper quadrant muscles following facial palsy in comparison with a control program. The key components of an immersive Virtual Reality (VR) based system and its effectiveness on facial palsy rehabilitation has been described in the form of experimental findings. Experimental trial was performed on an individual with facial upper quadrant muscles weakness due to facial palsy in a crossover study methodology with and without VR. EMG patterns from the facial upper quadrant muscles were recorded and analyzed for results. This trial has plotted a positive relationship between VR and facial upper quadrant muscles activation following a neurological impetus. The results reported here also show a consistent transfer of movement kinematics between physical and virtual tasks. EMG analysis has shown progressing improvement in the muscle activation in response to the challenging and impulsive activities in the virtual environment provided by the immersive VR devise.
    DOI/handle
    http://dx.doi.org/10.1109/SAI.2015.7237227
    http://hdl.handle.net/10576/54675
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    • Computer Science & Engineering [‎2428‎ items ]
    • Information Intelligence [‎98‎ items ]

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