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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

Normalized errors. The normalized errors depending on the training section for the TD+AR/LDA algorithm (a) and the WT/SVM-OVO (b).
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Figure 7: Normalized errors. The normalized errors depending on the training section for the TD+AR/LDA algorithm (a) and the WT/SVM-OVO (b).

Mentions: These normalized errors reveal the relative performances of the training sections, and allow the results for each subject to be displayed on the same scale. Figure 7 depicts the mean across subjects of the normalized errors for each training section, as well as the results for each subject. The relative performance of the training sections confirmed the trend of the non-normalized error observed in Figure 4, and the individual representations are in most cases well clustered around the mean for each training section.


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)

Normalized errors. The normalized errors depending on the training section for the TD+AR/LDA algorithm (a) and the WT/SVM-OVO (b).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Normalized errors. The normalized errors depending on the training section for the TD+AR/LDA algorithm (a) and the WT/SVM-OVO (b).
Mentions: These normalized errors reveal the relative performances of the training sections, and allow the results for each subject to be displayed on the same scale. Figure 7 depicts the mean across subjects of the normalized errors for each training section, as well as the results for each subject. The relative performance of the training sections confirmed the trend of the non-normalized error observed in Figure 4, and the individual representations are in most cases well clustered around the mean for each training section.

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