The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges

900 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Gesture recognition by instantaneous surface EMG images (2016, positive): “We adopted (1) ReLU non-linearity28 after each hidden layer, (2) batch normalization38 after the input and before each non-linearity, and (3) dropout39 with a probability of 0.”
Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation (2017, background): “At our sampling rate, 150 frames is equivalent to 150 ms, which is the window size suggested by several studies of pattern recognition based on prosthetic control [11,71,72].”
Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands (2016, methodology): “Proportional and simultaneous control of a large number of degrees of freedom of the prosthesis can allow achieving more natural and dexterous control using unsupervised or supervised methods (Fougner et al., 2012; Farina et al., 2014).”
Deep Learning for EMG-based Human-Machine Interaction: A Review (2021, methodology): “The processing step contains preprocessing, and feature extraction [77], which is similar to the machine learning method, and thus they share the same drawbacks of feature engineering.”