Abstract
Recent research has demonstrated that surface electromyography (sEMG) signals have non-Gaussianity and non-linearity properties. It is known that more muscle motor units are recruited and firing rates (FRs) increase as exertion increases. A hypothesis was proposed that the Gaussianity test (S (g)) and linearity test (S (ℓ)) levels of sEMG signals are associated with the number of active motor units (nMUs) and the FR. The hypothesis has only been preliminarily discussed in experimental studies. We used a simulation sEMG model involving spatial (active MUs) and temporal (three FRs) information to test the hypothesis. Higher-order statistics (HOS) from the bi-frequency domain were used to perform S (g) and S (ℓ). Multivariate covariance analysis and a correlation test were employed to determine the nMUs-S (g) relationship and the nMUs-S (ℓ) relationship. Results showed that nMUs, the FR, and the interaction of nMUs and the FR all influenced the S (g) and S (ℓ) values. The nMUs negatively correlated to both the S (g) and S (ℓ) values. That is, at the three FRs, sEMG signals tended to a more Gaussian and linear distribution as exertion and nMUs increased. The study limited experiment factors to the sEMG non-Gaussianity and non-linearity levels. The study quantitatively described nMUs and the FR of muscle that are not directly available from experiments. Our finding has guiding significance for muscle capability assessment and prosthetic control.
Highlights
Recent research has demonstrated that surface electromyography signals have non-Gaussianity and non-linearity properties
A hypothesis was proposed that the Gaussianity test (Sg) and linearity test (Sl) levels of surface electromyography (sEMG) signals are associated with the number of active motor units and the firing rates (FRs)
Kaplanis et al [8] found in step contraction tests conducted for a 5-s period that biceps brachii (BB) sEMG signals tended towards a more Gaussian distribution and a less linear distribution at 70% of MVC compared with 10%, 30%, 50% and 100% of MVC
Summary
Recent research has demonstrated that surface electromyography (sEMG) signals have non-Gaussianity and non-linearity properties. A hypothesis was proposed that the Gaussianity test (Sg) and linearity test (Sl) levels of sEMG signals are associated with the number of active motor units (nMUs) and the FR. Applying step contraction tests for a period of 5 s, Nazrpour et al [9] demonstrated that the non-Gaussianity of BB sEMG signals below 25% of MVC was significant. Kaplanis et al [8] found in step contraction tests conducted for a 5-s period that BB sEMG signals tended towards a more Gaussian distribution and a less linear distribution at 70% of MVC compared with 10%, 30%, 50% and 100% of MVC. A hypothesis is proposed that non-Gaussianity and non-linearity levels for sEMG signals are associated with nMUs and FRs. Reviewing prior Gaussianity and linearity tests of sEMG signals, we found only experimental studies. The finding of this study has practical guiding significance for the assessment of muscle activity in the field of occupational medicine and has good potential application for improving prosthetic control in the field of rehabilitation engineering
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