In this work a novel approach to realtime muscular fatigue detection is presented. Surface ElectroMyoGraphy (sEMG) has been used to monitor muscles work and in particular to detect signs of muscular fatigue. The joint estimation of a pair of electrical indicators (i.e. amplitude and mean spectral frequency of sEMG signal) is the basis for the detection of the muscular status, since their values are strictly influenced by different conditions of force production and fatigue occurrence. These indicators are estimated by adaptive algorithms specifically devised to process signals recorded during either static or dynamic conditions. The algorithms allow real-time processing and are integrated into a single monitor for muscular status. The monitor has been tested on signals recorded during spinning training sessions. Ten able body subjects volunteered for these sessions composed of several tasks characterized by different body postures and flying wheel resistances. A movement analysis system (StepPC©, DEMItaly), has been used to record cardiac activity, sEMG signal from rectus femoris and angular displacement at knee joint. Preliminary results demonstrate the feasibility of the approach and its capabilities in characterising the evolution of effort and fatigue during extended, sub-maximal training events.

Conforto, S., Bibbo, D., Schmid, M., & Dalessio, T. (2005). Muscular Fatigue from Electromyographic Recordings: Real-Time Monitoring during Exercise Training. In Proceedings of the 3rd European Medical and Biological Engineering Conference.

Muscular Fatigue from Electromyographic Recordings: Real-Time Monitoring during Exercise Training

CONFORTO, SILVIA;BIBBO, DANIELE;SCHMID, Maurizio;
2005

Abstract

In this work a novel approach to realtime muscular fatigue detection is presented. Surface ElectroMyoGraphy (sEMG) has been used to monitor muscles work and in particular to detect signs of muscular fatigue. The joint estimation of a pair of electrical indicators (i.e. amplitude and mean spectral frequency of sEMG signal) is the basis for the detection of the muscular status, since their values are strictly influenced by different conditions of force production and fatigue occurrence. These indicators are estimated by adaptive algorithms specifically devised to process signals recorded during either static or dynamic conditions. The algorithms allow real-time processing and are integrated into a single monitor for muscular status. The monitor has been tested on signals recorded during spinning training sessions. Ten able body subjects volunteered for these sessions composed of several tasks characterized by different body postures and flying wheel resistances. A movement analysis system (StepPC©, DEMItaly), has been used to record cardiac activity, sEMG signal from rectus femoris and angular displacement at knee joint. Preliminary results demonstrate the feasibility of the approach and its capabilities in characterising the evolution of effort and fatigue during extended, sub-maximal training events.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/178324
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