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Fatigue Analysis of Triceps Brachii Muscle using sEMG Signals and Recurrence Quantification Technique

Kiran Marri and Ramakrishnan Swaminathan
Non-Invasive Imaging and Diagnostics laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai India 600036

Abstract—Analysis of surface electromyography (sEMG) signals under fatigue conditions in dynamic contraction is gaining clinical relevance in the field of rehabilitation, ergonomics and sports performance. In this work, an attempt is made to analyze the sEMG signals recorded from triceps brachii muscles using Recurrence Quantification Analysis (RQA). The signals are recorded from 21 healthy adults during dynamic contraction involving dumbbell curl exercise. The sEMG signals are pre-processed and segmented into six equal zones along the time scale. The first zone and sixth zone are considered as nonfatigue and fatigue condition respectively. The signals are then subjected to RQA for further analysis. Two standard RQA features namely determinism (DET) and maximum length of the vertical line (VMAX) are computed for analyzing nonfatigue and fatigue conditions. A new RQA feature known as complexity (CPX) is also introduced for sEMG signal analysis that is derived from determinism. The results of RQA features are statistically verified using ANOVA. All the three RQA features namely, DET, complexity and VMAX are found to be statistically highly significant. In the case of fatigue condition, DET and VMAX increased by 17% and 30% respectively. It appears that RQA method may be a useful technique in differentiating fatigue and nonfatigue conditions under varied dynamic muscle contractions.

Index Terms—muscle fatigue, triceps brachii, surface electromyography, recurrence quantification analysis

Cite: Kiran Marri and Ramakrishnan Swaminathan, "Fatigue Analysis of Triceps Brachii Muscle using sEMG Signals and Recurrence Quantification Technique," Journal of Life Sciences and Technologies, vol. 4, no. 2, pp. 44-48, December 2016. doi: 10.18178/jolst.4.2.44-48
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