Recurrence quantification analysis of surface electromyographic signal: Sensitivity to potentiation and neuromuscular fatigue

Morana, Claire; Ramdani, Sofiane; Perrey, Stéphane; Varray, Alain

doi:10.1016/j.jneumeth.2008.09.023

Cited by 46 publications

(51 citation statements)

References 54 publications

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“…This is due to the nature of sEMG; in particular when peripheral fatigue sets in, the dynamics of the sEMG changes degrading the pattern recognition capabilities [2]. Recent research that investigates the peripherals of localized muscle fatigue [14,17] conforms to our findings. However, not much research has been conducted apart from our past research [7,8] to automate the process of predicting muscle fatigue by identifying and quantifying the Transition-to Fatigue (peripheral fatigue).…”

Section: Validation! Classificationsupporting

confidence: 81%

A Novel Feature Assisting in the Prediction of sEMG muscle fatigue towards a wearable autonomous system

Almulla

Sepúlveda

2010

2010 IEEE 16th International Mixed-Signals, Sensors and Systems Test Workshop (IMS3TW)

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Surface Electromyography (sEMG) activity of the biceps muscle was recorded from ten subjects performing isometric contraction until fatigue. The signals were segmented into two parts (Non-Fatigue and Transition-to-Fatigue). The novel feature was extracted from two established muscle fatigue detection features (Instantaneous Median Frequency and Total Band Power of the signal). The proposed name for this feature is "ID spectro." Initial results of class separation using Davies Bouldin Index (DBI) showed encouraging results of 0.87 on average for all trials. Using the novel feature ID spectro for classification produced an average longitude classification (within subjects) of 85.72% with accuracy 0.77 and a cross classification (across subjects) with an average of 79%. Wilcoxon rank sum test quantified the gross change of both longitudinal and cross classification, giving a result of (p<0.0140). Comparison of the ID spectro with other sEMG fatigue features on the same datasets shows a significant improvement of 10.5% (p<0.0027) on longitudinal classification when using the ID spectro in classifying Non-fatigue and Transition-to-Fatigue segments of the signal.

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Section: Validation! Classificationsupporting

confidence: 81%

A Novel Feature Assisting in the Prediction of sEMG muscle fatigue towards a wearable autonomous system

Almulla

Sepúlveda

2010

2010 IEEE 16th International Mixed-Signals, Sensors and Systems Test Workshop (IMS3TW)

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show abstract

“…The authors speculated that these results can be related to a higher MU synchronization in people who train. Morana et al (2009), have used RQA to study muscle fatigue during a submaximal isometric exercise. They found that %DET was unchanged during the increase in power (due to potentiation), increased when the power decreased and was unchanged despite an increased central command, as reflected by an increased RMS/ M (RMS normalized to M-wave area).…”

Section: Recurrence Quantification Analysis (Rqa)mentioning

confidence: 99%

Electromyographic models to assess muscle fatigue

González-Izal

Malanda

Gorostiaga

et al. 2012

Journal of Electromyography and Kinesiology

246

191

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“…Refrence ID RMS (Basmajian & De Luca, 1985;Kumar & Mital, 1996) (Cohen, 1995;Raez et al, 2006;Ricamato et al, 1992) Gabor Transform (Gabor, 1946) Wavelet analysis (Kumar et al, 2003;Laterza & Olmo, 1997) Autogression analysis (Graupe & Cline, 1975;Kim et al, 2005;Tohru, 1992) Entropy (Jaynes, 1957;Sung et al, 2008) Recurrence Quantification Analysis (Filligoi et al, 2010;Morana et al, 2009) HOS (Hussain et al, 2008Kanosue et al, 1979) Composite Features (Boostani & Moradi, 2003;Hudgins et al, 1993;Phinyomark et al, 2009) Table 1. Signal analysis and feature characteristics…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

“…Morana et al (2009) recently used recurrence quantification analysis in a study of muscle fatigue and stated that this method can be used to detect peripheral muscle fatigue.…”

Section: Recurrence Quantification Analysismentioning

confidence: 99%