Publication year
2007Source
IEEE Transactions on Biomedical Engineering, 54, 4, (2007), pp. 751-4ISSN
Publication type
Article / Letter to editor
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Organization
Neurology
Journal title
IEEE Transactions on Biomedical Engineering
Volume
vol. 54
Issue
iss. 4
Page start
p. 751
Page end
p. 4
Subject
DCN 2: Functional Neurogenomics; UMCN 3.1: Neuromuscular development and genetic disordersAbstract
Accurate force prediction from surface electromyography (EMG) forms an important methodological challenge in biomechanics and kinesiology. In a previous study (Staudenmann et al., 2006), we illustrated force estimates based on analyses lent from multivariate statistics. In particular, we showed the advantages of principal component analysis (PCA) on monopolar high-density EMG (HD-EMG) over conventional electrode configurations. In the present study, we further improve force estimates by exploiting the correlation structure of the HD-EMG via independent component analysis (ICA). HD-EMG from the triceps brachii muscle and the extension force of the elbow were measured in 11 subjects. The root mean square difference (RMSD) and correlation coefficients between predicted and measured force were determined. Relative to using the monopolar EMG data, PCA yielded a 40% reduction in RMSD. ICA yielded a significant further reduction of up to 13% RMSD. Since ICA improved the PCA-based estimates, the independent structure of EMG signals appears to contain relevant additional information for the prediction of muscle force from surface HD-EMG.
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