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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

Electrode positions. Schematic views of the position of the electrodes: (a) lateral, (b) transversal.
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Figure 1: Electrode positions. Schematic views of the position of the electrodes: (a) lateral, (b) transversal.

Mentions: The experimental protocol focused on a 9-class problem involving hand and wrist motions designed for trans-radial prostheses. The 9 classes were: wrist flexion, wrist extension, forearm supination, forearm pronation, thumb close, 4-finger close, making a fist, fingers spread open, and no motion (relax). Six pairs of Ag/AgCl surface electrodes (Ambu® Neuroline 720 01-K/12, Ambu A/S, Denmark) were mounted around the dominant forearm at equal distances from each other, one third distal from the elbow joint (Figure 1). The surface EMG data were recorded in bipolar derivations, amplified with a gain of 2000 (EMG-16, OT Bioelectronica, Italy), filtered between 47 and 440 Hz, and sampled at 1024 Hz. The reference electrode was placed on the non-dominant forearm. In each experimental session, the subject was instructed to perform the 9 classes of motion twice, in random order. Each contraction was 10 s in duration, with 3 s resting periods between consecutive contractions. Each subject performed three sessions on the same day, with 5-min breaks between the sessions to minimize fatigue. The rest periods between contractions and sessions were determined according to pilot tests and subjective evaluation of the subjects on the fatigue level. In total, 54 contractions (6 per class) were performed by each subject. In each contraction, the subject was instructed to start from the rest position, to reach the target position in 3 s, to maintain the target position for 4 s, and to return to the rest position in 3 s. Thus, in each contraction, one segment of static portion (4 s in the middle), and two segments of dynamic (anisotonic and anisometric, representing the two main dynamic situations in real movements) portion (3 s at each end) were obtained. These dynamic portions contained the full path between the rest and the target position. No feedback was provided to the subjects to regulate the position, but visual validation of the motions was performed by the experimenter. A user interface was used to provide the subject with the necessary visual prompt.


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)

Electrode positions. Schematic views of the position of the electrodes: (a) lateral, (b) transversal.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Electrode positions. Schematic views of the position of the electrodes: (a) lateral, (b) transversal.
Mentions: The experimental protocol focused on a 9-class problem involving hand and wrist motions designed for trans-radial prostheses. The 9 classes were: wrist flexion, wrist extension, forearm supination, forearm pronation, thumb close, 4-finger close, making a fist, fingers spread open, and no motion (relax). Six pairs of Ag/AgCl surface electrodes (Ambu® Neuroline 720 01-K/12, Ambu A/S, Denmark) were mounted around the dominant forearm at equal distances from each other, one third distal from the elbow joint (Figure 1). The surface EMG data were recorded in bipolar derivations, amplified with a gain of 2000 (EMG-16, OT Bioelectronica, Italy), filtered between 47 and 440 Hz, and sampled at 1024 Hz. The reference electrode was placed on the non-dominant forearm. In each experimental session, the subject was instructed to perform the 9 classes of motion twice, in random order. Each contraction was 10 s in duration, with 3 s resting periods between consecutive contractions. Each subject performed three sessions on the same day, with 5-min breaks between the sessions to minimize fatigue. The rest periods between contractions and sessions were determined according to pilot tests and subjective evaluation of the subjects on the fatigue level. In total, 54 contractions (6 per class) were performed by each subject. In each contraction, the subject was instructed to start from the rest position, to reach the target position in 3 s, to maintain the target position for 4 s, and to return to the rest position in 3 s. Thus, in each contraction, one segment of static portion (4 s in the middle), and two segments of dynamic (anisotonic and anisometric, representing the two main dynamic situations in real movements) portion (3 s at each end) were obtained. These dynamic portions contained the full path between the rest and the target position. No feedback was provided to the subjects to regulate the position, but visual validation of the motions was performed by the experimenter. A user interface was used to provide the subject with the necessary visual prompt.

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