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Characterization of the mechanomyographic signal of three different muscles and at different levels of isometric contractions

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Jotta B. , Garcia M. A. C. , Pino A. V. , Souza M. N. d.

tom Vol. 17, no. 4

73--84

Purpose: Lateral (X) and longitudinal (Y) mechanical oscillations of muscle fibers that take place during muscular contraction seem to contain information additionally to the myoelectric activity, which can contribute to the interpretation of some muscle gradation force mechanisms. However, no previous study was found that had investigated the relationship between the muscle force and features associated to the mechanomyographic (MMG) signal obtained by means of a biaxial accelerometer in three different muscles. Therefore, the aim of this study was to evaluate the relationship between the force output at different load levels (20% to 100%) of the maximum voluntary isometric contraction (%MVIC) and the two signals supplied by a biaxial accelerometer and, in addition, the so-called resultant (R) acceleration signal derived from the two signals mentioned previously. Twenty seven male volunteers participated in this study. Methods: The force output related to the right biceps brachii, soleus and gastrocnemius medialis muscles was studied by means of linear regression models fit to log-transformed of the root mean square (RMS) values of the MMG signals in X, Y, and R axes versus each %MVIC. The phase angle of R acceleration (PhaseR) and anthropometric data were also considered. Results: The angular coefficient a and the antilog of y-intercept b from the log-transformed of MMG data values versus force output were able to distinguish partially motor unit strategies during isometric contractions in the three muscles studied. Conclusion: The findings suggest that biaxial accelerometer seems to be an interesting approach in the assessment of muscle contraction properties.

Electromyography and mechanomyography signal recognition: Experimental analysis using multi-way array decomposition methods

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Wołczowski A. , Zdunek R.

tom Vol. 37, no. 1

103--113

In this study, we considered the problem of controlling a prosthetic hand with noisy electromyography (EMG) and mechanomyography (MMG) signals. Several dimensionality reduction methods were analyzed to assess their efficiency at classifying these signals, which were registered during the performance of grasping movements with various objects. Using the cross-validation technique, we compared various dimensionality reduction methods, such as principal components analysis, nonnegative matrix factorization, and some tensor decomposition models. The experimental results demonstrated that the high-est classification accuracy (exceeding 95% for all subjects when classifying 11 grasping movements) and lowest computational complexity were obtained when higher-order singular value decomposition was applied to a multi-way array of multi-channel spectrograms, where the temporal EMG/MMG signals from all channels were concatenated.