A new tool for the automatic detection of muscular voluntary contractions in the analysis of electromyographic signals. Pimentel, A., Gomes, R., Olstad, B., H., & Gamboa, H. Interacting with Computers, 27(5):492-499, 2015. abstract bibtex Electromyographic (EMG) signals play a key role in many clinical and\nbiomedical applications. They can be used for identifying patients with\nmuscular disabilities, assessing lower-back pain, kinesiology and motor\ncontrol. There are three common applications of the EMG signal: (1) to\ndetermine the activation timing of the muscle; (2) to estimate the force\nproduced by the muscle and (3) to analyze muscular fatigue through\nanalysis of the frequency spectrum of the signal. We have developed an\nEMG tool that was incorporated in an existing web-based biosignal\nacquisition and processing framework. This tool can be used on a\npost-processing environment and provides not only frequency and time\nparameters, but also an automatic detection of starting and ending times\nfor muscular voluntary contractions using a threshold-based algorithm\nwith the inclusion of the Teager-Kaiser energy operator. The algorithm\nfor the muscular voluntary contraction detection can also be reported\nafter a real-time acquisition, in order to discard possible outliers and\nsimultaneously compare activation times in different muscles. This tool\ncovers all known applications and allows a careful and detailed analysis\nof the EMG signal for both clinicians and researchers. The detection\nalgorithm works without user interference and is also user-independent.\nIt manages to detect muscular activations in an interactive process. The\nuser simply has to select the signal's time interval as input, and the\noutcomes are provided afterwards.
@article{
title = {A new tool for the automatic detection of muscular voluntary contractions in the analysis of electromyographic signals},
type = {article},
year = {2015},
identifiers = {[object Object]},
keywords = {Activation Detection,Electromyography,Human-computer interaction,Interactive Tool,Muscular voluntary contraction,Signal Processing},
pages = {492-499},
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abstract = {Electromyographic (EMG) signals play a key role in many clinical and\nbiomedical applications. They can be used for identifying patients with\nmuscular disabilities, assessing lower-back pain, kinesiology and motor\ncontrol. There are three common applications of the EMG signal: (1) to\ndetermine the activation timing of the muscle; (2) to estimate the force\nproduced by the muscle and (3) to analyze muscular fatigue through\nanalysis of the frequency spectrum of the signal. We have developed an\nEMG tool that was incorporated in an existing web-based biosignal\nacquisition and processing framework. This tool can be used on a\npost-processing environment and provides not only frequency and time\nparameters, but also an automatic detection of starting and ending times\nfor muscular voluntary contractions using a threshold-based algorithm\nwith the inclusion of the Teager-Kaiser energy operator. The algorithm\nfor the muscular voluntary contraction detection can also be reported\nafter a real-time acquisition, in order to discard possible outliers and\nsimultaneously compare activation times in different muscles. This tool\ncovers all known applications and allows a careful and detailed analysis\nof the EMG signal for both clinicians and researchers. The detection\nalgorithm works without user interference and is also user-independent.\nIt manages to detect muscular activations in an interactive process. The\nuser simply has to select the signal's time interval as input, and the\noutcomes are provided afterwards.},
bibtype = {article},
author = {Pimentel, Angela and Gomes, Ricardo and Olstad, Bj??rn Harald and Gamboa, Hugo},
journal = {Interacting with Computers},
number = {5}
}
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