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Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses.

Lorrain T, Jiang N, Farina D - J Neuroeng Rehabil (2011)

Bottom Line: A 9 class experiment was designed involving both static and 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University, Göttingen, Germany.

ABSTRACT

Background: 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.

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Related in: MedlinePlus

Error rates on static training. Error rate (mean and standard deviation) of the combinations feature set and classifier when training on the static part.
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Figure 4: Error rates on static training. Error rate (mean and standard deviation) of the combinations feature set and classifier when training on the static part.

Mentions: Figure 4 displays the error rate of each pair of feature set and classifier when the training was exclusively performed on the static part of the contractions. Using this training set, when combined with a threshold, a simple LDA classifier with a TD+AR feature set achieved, on average, more than 88% accuracy in dynamic situations. The use of a more complex classifier (SVM-OVR) and feature set (WT) slightly improved the performance (~1% increase in accuracy). Figure 4 also indicates that the LDA classifier is more compatible with the TD+AR feature set than with the WT feature set. Indeed, the use of the marginals, which is a non linear operator, reduces the compatibility with the linear nature of the LDA.


Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses.

Lorrain T, Jiang N, Farina D - J Neuroeng Rehabil (2011)

Error rates on static training. Error rate (mean and standard deviation) of the combinations feature set and classifier when training on the static part.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3113948&req=5

Figure 4: Error rates on static training. Error rate (mean and standard deviation) of the combinations feature set and classifier when training on the static part.
Mentions: Figure 4 displays the error rate of each pair of feature set and classifier when the training was exclusively performed on the static part of the contractions. Using this training set, when combined with a threshold, a simple LDA classifier with a TD+AR feature set achieved, on average, more than 88% accuracy in dynamic situations. The use of a more complex classifier (SVM-OVR) and feature set (WT) slightly improved the performance (~1% increase in accuracy). Figure 4 also indicates that the LDA classifier is more compatible with the TD+AR feature set than with the WT feature set. Indeed, the use of the marginals, which is a non linear operator, reduces the compatibility with the linear nature of the LDA.

Bottom Line: A 9 class experiment was designed involving both static and 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University, Göttingen, Germany.

ABSTRACT

Background: 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.

Show MeSH
Related in: MedlinePlus