Surface EMG in advanced hand prosthetics

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Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network (2017, background): “Surface electromyography (sEMG) has become a useful source of control signals for modern prosthetics due to its ease of use and non-invasiveness (Hargrove et al., 2007; Castellini and van der Smagt, 2009).”
Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands (2016, background): “It is common to see in the literature movement classification accuracy of up to 90%–95% (Castellini and van der Smagt, 2009; Tenore et al., 2009; Li et al., 2010; Peerdeman et al., 2011).”
Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation (2017, background): “The problem becomes challenging in realistic situations in which the trained model is used to recognize gestures during a new recording session, because sEMG signals are highly subject specific and vary considerably even between recording sessions of the same user within the same experimental paradi”