A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis.
IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
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引用次数: 0
Abstract
Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.
期刊介绍:
BPEX is an inclusive, international, multidisciplinary journal devoted to publishing new research on any application of physics and/or engineering in medicine and/or biology. Characterized by a broad geographical coverage and a fast-track peer-review process, relevant topics include all aspects of biophysics, medical physics and biomedical engineering. Papers that are almost entirely clinical or biological in their focus are not suitable. The journal has an emphasis on publishing interdisciplinary work and bringing research fields together, encompassing experimental, theoretical and computational work.