title: Influence of smoothing window length on electromyogram amplitude estimates.
abstract: A systematic, experimental study of the influence of smoothing window length on the signal-to-noise ratio (SNR) of electromyogram (EMG) amplitude estimates is described. Surface EMG waveforms were sampled during nonfatiguing, constant-force, constant-angle contractions of the biceps or triceps muscles, over the range of 10%-75% maximum voluntary contraction. EMG amplitude estimates were computed with eight different EMG processor schemes using smoothing length durations spanning 2.45-500 ms. An SNR was computed from each amplitude estimate (deviations about the mean value of the estimate were considered as noise). Over these window lengths, average +/- standard deviation SNR's ranged from 1.4 +/- 0.28 to 16.2 +/- 5.4 for unwhitened single-channel EMG processing and from 3.2 +/- 0.7 to 37.3 +/- 14.2 for whitened, multiple-channel EMG processing (results pooled across contraction level). It was found that SNR increased with window length in a square root fashion. The shape of this relationship was consistent with classic theoretical predictions, however none of the processors achieved the absolute performance level predicted by the theory. These results are useful in selecting the length of the smoothing window in traditional surface EMG studies. In addition, this study should contribute to the development of EMG processors which dynamically tune the smoothing window length when the EMG amplitude is time varying.
authors.affiliation: Mechanical Engineering Department, Laval University, Québec, Canada.
authors.forename: Y
authors.initials: Y
authors.lastname: St-Amant
authors.forename: D
authors.initials: D
authors.lastname: Rancourt
authors.forename: E A
authors.initials: EA
authors.lastname: Clancy
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D004566:Electrodes; D004576:Electromyography; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009068:Movement; D009119:Muscle Contraction; D012016:Reference Values; D012815:Signal Processing, Computer-Assisted; D013499:Surface Properties