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The neural network architecture and pre-training methodology used to improve the performance of a deep learning-based HD-sEMG decomposition algorithm. The neural network consists of a single gated recurrent unit layer, with predictions made using a 20-sample wide window of the hidden vector output, which is flattened before being passed to a sigmoid-activated densely-connected linear layer. In the pre-training phase a multitask learning regimen is used to optimise the parameters of the gated recurrent unit using the simulated sEMG. This pre-trained layer can then be used to improve the optimisation performance on real sEMG data.

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