Strategies to identify muscle fatigue from SEMG during cycling

Singh, V, Kumar, D, Djuwari, D, Polus, B, Fraser, S, Hawley, J and Lo Giudice, S 2004, 'Strategies to identify muscle fatigue from SEMG during cycling', in M. Palaniswami (ed.) Proceedings of the 2004 Intelligent Sensors, Sensor Networks & Information Processing Conference, Melbourne, Australia, 14-17 December 2004, pp. 547-551.


Document type: Conference Paper
Collection: Conference Papers

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Title Strategies to identify muscle fatigue from SEMG during cycling
Author(s) Singh, V
Kumar, D
Djuwari, D
Polus, B
Fraser, S
Hawley, J
Lo Giudice, S
Year 2004
Conference name Intelligent Sensors, Sensor Networks & Information Processing Conference
Conference location Melbourne, Australia
Conference dates 14-17 December 2004
Proceedings title Proceedings of the 2004 Intelligent Sensors, Sensor Networks & Information Processing Conference
Editor(s) M. Palaniswami
Publisher IEEE
Place of publication Piscataway, USA
Start page 547
End page 551
Total pages 5
Abstract Detection, quantification and analysis of muscle fatigue are crucial in occupational/rehabilitation and sporting settings. Sports organizations, such as the Australian Institute of Sports (AIS), currently monitor fatigue by a battery of tests including invasive techniques that require taking blood samples and/or muscle biopsies, the latter of which is highly invasive, painful, time consuming and expensive. SEMG (surface electromyography) is non-invasive monitoring of muscle activation and is an indication of localized muscle fatigue based on the observed shift of the power spectral density of the SEMG. The success of SEMG based techniques is currently limited to isometric contraction and is not acceptable to the human movement community. The paper proposes and tests a simple signal processing technique to identify the onset of muscle fatigue during cyclic activities of muscles, such as VL and VM, during cycling. Based on experiments conducted with 7 participants, using power output as a measure of fatigue, the technique is able to identify muscle fatigue with 98% significance.
Subjects Biomedical Engineering not elsewhere classified
DOI - identifier 10.1109/ISSNIP.2004.1417520
Copyright notice © 2004 IEEE
ISBN 0-7803-8894-1
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