Surface EMG classification during dynamic contractions for multifunction transradial prostheses

High usability myo-controlled devices require robust classification schemes during dynamic contractions. Therefore, this study investigates the impact of the training data set on the performance of several pattern recognition algorithms during dynamic contractions. It is shown that combined with a threshold to detect the onset of the contraction, current pattern recognition algorithms used on static conditions can maintain relatively high classification accuracy on dynamic situations. Moreover, the performance of the pattern recognition algorithms tested improved by optimizing the choice of the training set. Finally, the results also showed that rather simple approaches for classification of time-domain features provide results comparable to more complex classification methods of wavelet features.