Rate of agreement (%) between the neural network MU activity predictions and the decomposition algorithm on one second of wrist flexor HD-sEMG signal. Data is presented as median values over 39 motor units from nine participants +/- the interquartile range on the bounds of the box, with the lowest and highest values as the whiskers. Both outputs were converted to timestamps using a two class K-means clustering. The neural network using a gated recurrent unit (GRU) network that was pre-trained using simulated EMG signal significantly outperformed a GRU with random initialisation (two-tailed Wilcoxon signed-rank test, Z = 4.0, p = 0.00006, median difference 8.1 using Hodges-Lehmann estimator, 95% CI 3.4 to 13.3 using method of Walsh averages).
