Article

For high usability, myo-controlled devices require robust
classification schemes during dynamic contractions. Therefore, this study
investigates the impact of the training data set in the performance of several
pattern recognition algorithms during dynamic contractions. METHODS: A 9 class
experiment was designed involving both static and dynamic situations. The
performance of various feature extraction methods and classifiers was evaluated
in terms of classification accuracy. RESULTS: It is shown that, combined with a
threshold to detect the onset of the contraction, current pattern recognition
algorithms used on static conditions provide relatively high classification
accuracy also on dynamic situations. Moreover, the performance of the pattern
recognition algorithms tested significantly 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. CONCLUSIONS: Non-stationary
surface EMG signals recorded during dynamic contractions can be accurately
classified for the control of multi-function prostheses.

Langue : ANGLAIS