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Preferred sensor sites for surface EMG signal decomposition
- Source :
- Physiological measurement. 33(2)
- Publication Year :
- 2012
-
Abstract
- Technologies for decomposing the electromyographic (EMG) signal into its constituent motor unit action potential trains have become more practical by the advent of a non-invasive methodology using surface EMG (sEMG) sensors placed on the skin above the muscle of interest (De Luca et al 2006 J. Neurophysiol. 96 1646-57 and Nawab et al 2010 Clin. Neurophysiol. 121 1602-15). This advancement has widespread appeal among researchers and clinicians because of the ease of use, reduced risk of infection, and the greater number of motor unit action potential trains obtained compared to needle sensor techniques. In this study we investigated the influence of the sensor site on the number of identified motor unit action potential trains in six lower limb muscles and one upper limb muscle with the intent of locating preferred sensor sites that provided the greatest number of decomposed motor unit action potential trains, or motor unit yield. Sensor sites rendered varying motor unit yields throughout the surface of a muscle. The preferred sites were located between the center and the tendinous areas of the muscle. The motor unit yield was positively correlated with the signal-to-noise ratio of the detected sEMG. The signal-to-noise ratio was inversely related to the thickness of the tissue between the sensor and the muscle fibers. A signal-to-noise ratio of 3 was found to be the minimum required to obtain a reliable motor unit yield.
- Subjects :
- Motor unit action potential
Adolescent
Physiology
Surface Properties
Biomedical Engineering
Biophysics
Upper limb muscle
Action Potentials
Electromyography
Signal-To-Noise Ratio
Signal
Lower limb
Article
Young Adult
Signal-to-noise ratio
Physiology (medical)
medicine
Humans
Electrodes
Mathematics
Motor Neurons
medicine.diagnostic_test
Muscles
Signal Processing, Computer-Assisted
Anatomy
Motor unit
Skinfold Thickness
Regression Analysis
medicine.symptom
Biomedical engineering
Muscle contraction
Muscle Contraction
Subjects
Details
- ISSN :
- 13616579
- Volume :
- 33
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- Physiological measurement
- Accession number :
- edsair.doi.dedup.....c4b649c5afdf1330c2e34ebb11c0a632