A nonlinearities inverse distance weighting spatial interpolation approach applied to the surface electromyography signal

Abstract

Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spatial interpolation methods for estimation: Inverse distance weighted (IDW) and Kriging. Furthermore, a new technique is proposed using a modified nonlinearity formula based on IDW. A set of EMG signals recorded from the noninvasive multi-electrode grid from different types of subjects, sex, age, and type of muscles have been studied when muscles are under regular tension activity. A goodness of fit measure (R2) is used to evaluate the proposed technique. The interpolated signals are compared with the actual signals; the Goodness of fit measure's value is almost 99%, with a processing time of 100msec. The resulting technique is shown to be of high accuracy and matching of spatial interpolated signals to actual signals compared with IDW and Kriging techniques.

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“…Inverse distance weighting (IDW) and Kriging interpolation are examples of widely used spatial interpolation methods. In some cases, the performance of the Kriging technique outperforms that of the IDW [21]- [23], so we will use Kriging interpolation in this research for evaluation. For each region, the test was applied in several scenarios: daily, monthly, three-month period, and six-month period.…”