Key result
Muscle fatigue significantly increased relevant phasic electrodermal activity features, allowing a KNN classifier to discriminate between fatigued and non-fatigued subjects with 75.69% accuracy.
Why the study?
Can electrodermal activity (EDA) analysis accurately detect muscle fatigue during isometric contraction in healthy subjects?
Observational (n=32)
Can electrodermal activity (EDA) analysis accurately detect muscle fatigue during isometric contraction in healthy subjects?
Electrodermal activity analysis can serve as a correlate of muscle fatigue, providing integrative information to standard electromyography indices.
EDA may indicate muscle fatigue in healthy subjects; leaves open validation versus EMG in patients.
We studied the effects of muscle fatigue on the Autonomic Nervous System (ANS) dynamics. Specifically, we monitored the electrodermal activity (EDA) on 32 healthy subjects performing isometric biceps contraction. As assessed by means of an electromyography (EMG) analysis, 15 subjects showed muscle fatigue and 17 did not. EDA signals were analyzed using the recently proposed cvxEDA model in order to decompose them into their phasic and tonic components and extract effective features to study ANS dynamics. A statistical comparison between the two groups of subjects was performed. Results revealed that relevant phasic EDA features significantly increased in the fatigued group. Moreover, a pattern recognition system was applied to the EDA dataset in order to automatically discriminate between fatigued and non-fatigued subjects. The proposed leave-one-subject-out KNN classifier showed an accuracy of 75.69%. These results suggest the use of EDA as correlate of muscle fatigue, providing integrative information to the standard indices extracted from the EMG signals.
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Greco et al. (2017) conducted an observational in Healthy subjects (n=32). Muscle fatigue vs. Non-fatigued subjects was evaluated on Electrodermal activity (EDA) features and classification accuracy. Muscle fatigue significantly increased relevant phasic electrodermal activity features, allowing a KNN classifier to discriminate between fatigued and non-fatigued subjects with 75.69% accuracy.
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