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Impact Factor: 1.211
ISSN Print: 1609-0985
ISSN Online: 2199-4757
Imprint: Springer
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latest updated: 2015 / 07 / 30
Wrist Movement Characterization by Mechanomyography Technique
Vera Lúcia da Silveira Nantes Button, 
Eduardo Mendonça Scheeren, 
Eddy Krueger- Beck, 
Guilherme Nunes Nogueira-Neto, 
Percy Nohama
Mechanomyography (MMG) monitors the oscillations caused by muscle contraction. Muscle contractions generate motion artifacts which can significantly affect signal processing of MMG. In order to avoid such interference during signal analysis, one can respect a time delay (TD) after the onset of contraction (OC). This study aimed to identify wrist antagonist movements from signals recorded with MMG sensors. The distinction was performed after evaluating the correlation between windows of interest in signals between 0.2 s after OC (0.2 AOC) and 1.0 s after OC (1.0 AOC). Twelve volunteers performed concentric wrist contractions: flexion and ulnar deviation (inner side) and extension and radial deviation (outer side). Two triaxial MMG sensors were placed on the inner and outer sides of the forearm. The features used in the analysis of MMG signals were root mean square (RMS), number of zero-crossings, peak counting and the multiplication RMS times number of zero-crossings (RZ). Two analysis window lengths (0.25 s and 0.5 s) were evaluated. ANOVA tests indicated that flexion was different from extension, ulnar and radial deviation, and radial deviation was different from ulnar deviation and flexion. The results suggest that it is possible to identify whether the movements are antagonistic and to distinguish movements from the same side. There were strong correlations between the acquired signals and TD, mainly the devised RZ feature (0.81~0.95). This study proposed an approach to determine whether triaxial MMG features can be used for motor prosthesis control. The triaxial modulus presented strong correlations for all movements and can be useful in future applications.
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