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） increa...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 hy- pothesis was proposed that the Gaussianity test （Sg） and linearity test （St） levels of sEMG signals are associated with the num- ber 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 Sg and St. Multivariate covariance analysis and a correlation test were employed to determine the nMUs-Sg relationship and the nMUs-St relationship. Results showed that nMUs, the FR, and the interaction of nMUs and the FR all influenced the Sg and St values. The nMUs negatively correlated to both the Sg and St 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.展开更多
基金supported by the National High Technology Research and Development Program of China and the National Basic Research Program of China (Grant No. 2011CB7000)
文摘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 hy- pothesis was proposed that the Gaussianity test （Sg） and linearity test （St） levels of sEMG signals are associated with the num- ber 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 Sg and St. Multivariate covariance analysis and a correlation test were employed to determine the nMUs-Sg relationship and the nMUs-St relationship. Results showed that nMUs, the FR, and the interaction of nMUs and the FR all influenced the Sg and St values. The nMUs negatively correlated to both the Sg and St 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.
