Muscle Fatigue Analysis During Welding Tasks Using sEMG and Recurrence Quantification Analysis

Muscle Fatigue Analysis During Welding Tasks Using sEMG and Recurrence Quantification Analysis

Ali Keshavarz Panahi, Sohyung Cho, Chris Gordon
Copyright: © 2021 |Pages: 16
DOI: 10.4018/IJAIE.287609
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Abstract

The main goal of this study was to detect muscle fatigue and to identify muscles vulnerable to musculoskeletal disorders by evaluating muscle activation of subjects during welding tasks. In this study, six subjects performed two different welding tasks for a total of three hours. Surface electromyography (sEMG) was used to record the muscle activation of sixteen different muscles. Recurrence Quantification Analysis (RQA) was then used to analyze the EMG data. In addition, a subjective fatigue assessment was conducted to draw comparisons with the RQA results. According to the RQA results, twelve of the tested muscles experienced fatigue by showing significant difference in RQA values (p-value < 0.05) between the first and last 10 minutes of the experiment. Moreover, time-to-fatigue results obtained from RQA and subjective analysis were closely correlated for seven muscle groups. This study showed that RQA can be used in ergonomic studies for evaluating muscle activation during construction tasks.
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Surface Electromyography (Semg):

The use of sEMG (Surface Electromyography) has been shown to be one of the most effective and quantitative methods to evaluate muscle activity and risk factors (Dennerlein et al., 2003; Farina, 2006; Lowe et al., 2001; Panahi & Cho, 2016; Sporrong et al., 1999). Moreover, sEMG offers a great advantage to be used as a real-time measurement method. There are two major physiological elements of the muscle that affect the EMG signal: reduction in conduction velocity in the muscle fibers and enhanced motor unit synchronization, the state in which motor units fire at the same time. These phenomena cause the frequency of the EMG signal to decrease and the amplitude to increase. These two phenomena, reduction in conduction velocity and increase in motor unit synchronization, are believed to be the myoelectric manifestations of muscle fatigue (Farina et al., 2002; Jensen et al., 2000; Li et al., 2004). Muscle fatigue analysis using sEMG has shown promising results in quantifying muscle activation for ergonomic purposes (Peppoloni et al., 2016).

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