document: •More efforts should be directed towards advancing the methods of feature extraction to overcome the influence of dynamic factors that limit the performance. The use of advanced machine learning methods such as deep neural networks and muscles synergies extraction should also be investigated on problems under the influence of multiple dynamic factors as such methods may provide substantial improvements upon the utilized time-and-frequency EMG feature extraction methods (Diener, Janke, & Schultz, 2015; Ison, Vujaklija, Whitsell, Farina, & Artemiadis, 2016; Park & Lee, 2016). Meanwhile, we showed that the performance of the learning algorithms can be improved by using feature extraction methods that rely on the angular information of muscle activation patterns. Features such as the TD-PSD and the DFT proved more successful than others in reducing the impact of the two dynamic factors that we considered in this paper. Such features can be readily implemented into a prosthesis controller for real-time control, especially that the EMG pattern recognition systems are nowadays becoming available for clinical testing, e.g. the COAPT complete control system (Kuiken et al., 2014)11https://www.coaptengineering.com/.
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