2009
DOI: 10.1590/s1413-35552009005000057
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Avaliação da fadiga muscular pela mecanomiografia durante a aplicação de um protocolo de EENM

Abstract: Background: Neuromuscular electrical stimulation (NMES) is a widely used technique for rehabilitation in physical therapy, however

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Cited by 21 publications
(4 citation statements)
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“…However, associated with the decreased of MF, a progressive decrease in the RMS in CP for all masticatory muscles was observed in the present study, with a significant difference after 5s of MVC. The reduction of RMS values after muscle fatigue was also observed in other studies (18,20,21) . Maton et al (18) observed a decrease in RMS while maintaining 90 to 100% of bite force and a relationship between the time of isometric resistance and the level of bite force.…”
Section: Discussionsupporting
confidence: 85%
“…However, associated with the decreased of MF, a progressive decrease in the RMS in CP for all masticatory muscles was observed in the present study, with a significant difference after 5s of MVC. The reduction of RMS values after muscle fatigue was also observed in other studies (18,20,21) . Maton et al (18) observed a decrease in RMS while maintaining 90 to 100% of bite force and a relationship between the time of isometric resistance and the level of bite force.…”
Section: Discussionsupporting
confidence: 85%
“…An isokinetic dynamometer (Cybex Norm 6000, Cybex Division of Lumex, Inc., Ronkonkoma, NY, USA) was used for torque measurement. It was set to isometric mode at an angle of 60° degrees of knee flexion 18,29,36,37 .…”
Section: Apparatusmentioning
confidence: 99%
“…During FES application, the electrical stimuli can interfere with EMG signals due to circuit limitations (Seki et al, 2003) in closed-loop systems (Venkatasubramanian et al, 2006). Because MMG is based on mechanical oscillations of muscles during contraction, it is not affected directly by electrical pulses yielded during FES application (Faller et al, 2009;Seki et al, 2003). MMG signals are viable as biofeedback to human-machine interface, such as a joystick (Xie and Dokos, 2013), myoelectrical prostheses Yu and Chang, 2010) or neuroprostheses control (Popovic and Thrasher, 2004) using temporal and spectral features (Beck et al, 2009;Stock et al, 2010a).…”
Section: Recognition To Neural/myoelectric Prosthesismentioning
confidence: 99%