Sensitivity evaluation of HOS parameters by data fusion from HD-sEMG grid

Abstract

The objective of this paper is to study the sensitivity of High Order statistics (HOS) parameters (the kurtosis and the Skewness) toward variation of the force intensity by applying different methods of data fusion. The data fusion allows us to obtain a single EMG signal or a single HOS parameter set from a 64 signals captured by an 8×8 High Density Surface EMG (HD-sEMG) grid. For this purpose, we started by calculating the HOS parameters (Kurtosis and Skewness) for the 64 monopolar signals for each one of three force intensities: 20%, 50% and 80% MVC. Then we applied two different data fusion procedures: Laplacian matrix coupled to Principle Component Analysis (PCA), and Laplacian matrix coupled with HOS parameter averaging. According to the obtained results, we noticed an important spatial sensitivity of the HOS parameters according to force variation for the monopolar grid. After data fusion, both studied techniques gave interesting results with better sensitivity for the Laplacian matrix combined to HOS parameter averaging method. Further studies are envisaged to assess the HOS parameter sensitivity to varying force and muscle anatomies.