Literature DB >> 9609944

Influence of smoothing window length on electromyogram amplitude estimates.

Y St-Amant1, D Rancourt, E A Clancy.   

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.

Mesh:

Year:  1998        PMID: 9609944     DOI: 10.1109/10.678614

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  5 in total

1.  Comparison of speed-accuracy tradeoff between linear and nonlinear filtering algorithms for myocontrol.

Authors:  Cassie N Borish; Adam Feinman; Matteo Bertucco; Natalie G Ramsy; Terence D Sanger
Journal:  J Neurophysiol       Date:  2018-01-31       Impact factor: 2.714

2.  Evaluation of EMG processing techniques using Information Theory.

Authors:  Fernando D Farfán; Julio C Politti; Carmelo J Felice
Journal:  Biomed Eng Online       Date:  2010-11-12       Impact factor: 2.819

3.  A Study on An EMG Sensor with High Gain and Low Noise for Measuring Human Muscular Movement Patterns for Smart Healthcare.

Authors:  Sun-Woo Yuk; In-Ho Hwang; Hyeon-Rae Cho; Sang-Geon Park
Journal:  Micromachines (Basel)       Date:  2018-10-29       Impact factor: 2.891

4.  Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition.

Authors:  Srikanth Sagar Bangaru; Chao Wang; Fereydoun Aghazadeh
Journal:  Sensors (Basel)       Date:  2020-09-15       Impact factor: 3.576

5.  Hybrid control combined with a voluntary biosignal to control a prosthetic hand.

Authors:  Saeed Bahrami Moqadam; Seyed Mohammad Elahi; An Mo; WenZeng Zhang
Journal:  Robotics Biomim       Date:  2018-09-19
  5 in total

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