A Laplacian Gaussian Mixture Model for Surface EMG Signals of Human Arm Activity

Abstract

The probability density function (pdf) of surface Electromyography (sEMG) signals follows any one of the standalone standard distributions: the Gaussian or the Laplacian. Further, the choice of the model is dependent on muscle contraction force (MCF) levels. Hence, a unified model is proposed which explains the statistical nature of sEMG signals at different MCF levels. In this paper, we propose the Laplacian Gaussian Mixture (LGM) model for the signals recorded from upper limbs. This model is able to explain the sEMG signals from different activities corresponding to different MCF levels. The model is tested on different bench-mark sEMG data sets and is validated using both the qualitative and quantitative perspectives. It is determined that for low and medium contraction force levels the proposed mixture model is more accurate than both the Laplacian and the Gaussian models. Whereas for high contraction force level, the LGM model behaves as a Gaussian model. The mixing weights of the LGM model are analyzed and it is observed that for low and medium MCF levels both the mixing weights of LGM model do contribute. Whereas for high contraction force levels the Laplacian weight becomes weaker. The proposed LGM model for sEMG signals from upper limbs explains sEMG signals at different MCF levels. The proposed model helps in improved understanding of statistical nature of sEMG signals and better feature representation in the classification problems.