DocumentCode :
1546307
Title :
Visualization of Trunk Muscle Synergies During Sitting Perturbations Using Self-Organizing Maps (SOM)
Author :
Milosevic, Matija ; McConville, Kristiina M Valter ; Sejdic, Ervin ; Masani, Kei ; Kyan, Matthew J. ; Popovic, Milos R.
Author_Institution :
Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada
Volume :
59
Issue :
9
fYear :
2012
Firstpage :
2516
Lastpage :
2523
Abstract :
The purpose of this study was to demonstrate the use of the self-organizing map (SOM) method for visualization, modeling, and comparison of trunk neuromuscular synergies during perturbed sitting. Thirteen participants were perturbed at the level of the sternum, in eight directions during sitting. Electromyographic (EMG) responses of ten trunk muscles involved in postural control were recorded. The SOM was used to encode the EMG responses on a 2-D projection (i.e., visualization). The result contains similar patterns mapped close together on the plot therefore forming clusters of data. Such visualization of ten EMG responses, following eight directional perturbations, allows for comparisons of direction-dependent postural synergies. Direction-dependent neuromuscular response models for each muscle were then constructed from the SOM visualization. The results demonstrated that the SOM was able to encode neuromuscular responses, and the SOM visualization showed direction-dependent differences in the postural synergies. Moreover, each muscle was modeled using the SOM-based method, and derived models showed that all muscles, except for one, produced a Gaussian fit for direction-dependent responses. Overall, SOM analysis offers a reverse engineering method for exploration and comparison of complex neuromuscular systems, which can describe postural synergies at a glance.
Keywords :
Data visualization; Electromyography; Mathematical model; Neuromuscular; Training; Vectors; Balance; electromyography (EMG); muscle synergy; perturbation; self-organizing map (SOM); sitting; visualization; Adult; Electromyography; Humans; Image Processing, Computer-Assisted; Male; Muscle, Skeletal; Posture; Regression Analysis; Signal Processing, Computer-Assisted; Torso;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2012.2205577
Filename :
6222320
Link To Document :
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