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IISE Annual Conference and Expo 2023, 1909p. (2023) DOI:10.21872/2023IISE_1909
Surface electromyography (EMG) is a non-invasive diagnostic tool used to reveal neuromuscular disorders. To standardize data comparisons, EMG signals must be normalized. The most commonly used method is the peak value of root-mean-square (RMS) smoothed EMG signal during maximum voluntary isometric contractions (MVC). However, this method only relies on the peak MVC value, and the selection of the RMS window-size can affect its reliability. Therefore, utilizing non-linear techniques such as Recurrence Quantification Analysis (RQA), is crucial when considering EMG as an inherently non-stationary, noisy time-series. The study aims to (i) investigate alternative EMG normalization techniques using RQA with dimensionality reduction methods (PCA, KPCA), and (ii) to quantify the differences in dynamical characteristics among MVC movements in the lower-limb muscles (vastus lateralis (VL), biceps femoris (BF), gastrocnemius lateralis (GL), tibialis anterior (TA)). Results indicated that the first principal components of PCA and KPCA were weakly correlated with peak MVC in BF, GL, and TA (0.30< ρ <0.50). In contrast, for VL, a moderate correlation was found between peak MVC and the first principal components (PC1: ρ=0.61, p<0.001; KPC1: ρ=0.60, p<0.001). This research provides valuable insight into how RQA can potentially improve the development of an alternative EMG normalization technique over traditional methods. Additionally, the principal components of RQA were able to detect the differences in dynamic characteristics better than peak MVC values under the same conditions, making RQA a useful tool for identifying differences between MVC movements.
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