A SNR-independent formulation of a double threshold algorithm for the estimation of muscle activation intervals

The aim of this work is to propose an improvement to the double threshold algorithm for muscular activation intervals estimation developed by Bonato and his co-workers. The proposed method has been designed in order to be adaptive also when the Signal to Noise ratio (SNR) of the sEMG signal changes during the trial, by re-evaluating the parameters of the algorithm according to the estimated local SNR and the desired detection and false alarm probabilities. This novel implementation is also suitable for working in pseudo real-time since it can give information on burst estimation shortly after the end of the current muscular activity. The proposed method was tested on simulated signals taking into account changes in the SNR during the trial, and results were compared with those obtained with the classical implementation of the algorithm.