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In myoelectric prosthesis design, there is often a trade-off between control robustness and range of executable movements. As a low movement error rate is necessary in any real application, this often results in a quite severe limitation on the dexterity of the user. One possible remedy for this could come from the use of multi-label machine learning methods, where complex hand movements can be ex
