Sampling, noise-reduction and amplitude estimation issues in surface electromyography.
By E. Clancy, E. Morin, Roberto Merletti. Journal of Electromyography & Kinesiology, 2002
674 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Signal Processing
View Paper: https://www.semanticscholar.org/paper/17c780c1aca508a3172fa0d5dd1bf10168a10f54
How researchers see this paper
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In Biomedical Engineering
- Angkoon Phinyomark
- Dario Farina
- Ajay Seth
- Ales Holobar
- Jennifer L. Hicks
- Apoorva Rajagopal
- Thomas K. Uchida
- Roger M. Enoka
In Signal Processing
- Dario Farina
- Roger M. Enoka
- Roberto Merletti
- Pornchai Phukpattaranont
- Angkoon Phinyomark
- Chusak Limsakul
- Ales Holobar
- Bernhard Graimann
Citations in the corpus
- A Novel Feature Extraction for Robust EMG Pattern Recognition (2009, methodology): “…three types of noise can be removed using typical filtering procedures such as band-pass filter, band-stop filter, or the use of well electrode and instrument [3-4] but the interferences of random noise that fall in EMG dominant frequency energy is difficult to be removed using previous procedures.”
- The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges (2014, background): “Other factors that reduce the practical accuracy of pattern recognition systems are the electrode shifts with doffing and donning of the prosthesis [31], [43], the changes in arm posture [32], [33], [44], the variability in EMG features over different days [63]–[66], and slow time-dependent changes,”
- Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement. (2015, background): “Since EMG is difficult to normalize and is subject to measurement error [147], you generally cannot compare EMG signals directly to predicted muscle activations from a simulation.”
- Methodological aspects of SEMG recordings for force estimation--a tutorial and review. (2010, background): “At low contraction levels, the noise contribution in the signal can be relatively large and limit the precision of EMG amplitude estimates (Clancy et al., 2002).”
- Surface EMG based muscle fatigue evaluation in biomechanics. (2009, methodology): “According to Clancy et al. (2002) the amplitude of the single channel sEMG signal can be estimated using cascade of five sequential processing stages: (1) noise rejection/filtering, (2) whitening, (3) amplitude demodulation, (4) smoothing and (5) relinearization (Fig.”
- Surface Electromyography Signal Processing and Classification Techniques (2013, background): “However, this type of artifact is attenuated by reducing the skin impedance [10].”
- Decoding the neural drive to muscles from the surface electromyogram. (2010, background): “These two estimators are equivalent in practical applications (Clancy et al., 2002).”
- Gesture recognition by instantaneous surface EMG images (2016, neutral): “The amplitude of the myoelectric signal at any instant may rapidly fluctuate between voltages above and below zero 8,9 and thus resembles a zero-mean random process whose standard deviation is proportional to the number of active motor units and the rate at which motor units are activated 11 .”