Use of recurrence quantification analysis in virtual reality training: A case study

The aim of the present study was to apply recurrence quantification analysis (RQA) to surface electromyographic (sEMG) signals during virtual reality training. It has been previously demonstrated that the percentage of determinism (%DET) assessed by RQA may be related to the synchronization of motor...

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Bibliographic Details
Main Authors: Vuong, Barry, McConville, Kristiina
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Costs
Electromyography
Fatigue
Muscles
Signal analysis
Surface fitting
Testing
Virtual environment
Virtual reality
Online Access:Request full text
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Summary:The aim of the present study was to apply recurrence quantification analysis (RQA) to surface electromyographic (sEMG) signals during virtual reality training. It has been previously demonstrated that the percentage of determinism (%DET) assessed by RQA may be related to the synchronization of motor units. The experiment consisted of three weeks of training using the Nintendo Wii Fit® software, Wii Fit balance board and the Nintendo Wii® system for a healthy male in his early twenties. Myoelectric signals were acquired from the right peroneus longus and soleus muscles. During the course of the virtual training, in-game balance tests and a soccer simulator were employed. There appeared to be a gradual decrease in %DET as the subject trained. As a result, it can be suggested that RQA may be a viable method for measuring motor learning during rehabilitation.
DOI:10.1109/TIC-STH.2009.5444379