Kumar Koppolu, P.Chemmangat, K.2026-02-032024Biomedical Physics and Engineering Express, 2024, 10, 6, pp. -https://doi.org/10.1088/2057-1976/ad773ahttps://idr.nitk.ac.in/handle/123456789/20847Hand 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. © 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.Artificial limbsEmpirical mode decompositionImage analysisImage codingImage segmentationPalmprint recognitionEnergy-entropyHands movementMachine-learningMode decompositionMotion artifactMovement recognitionMyoelectricPerformancePower line interference and motion artifactPowerline interferenceMyoelectrically controlled prostheticsadultalgorithmArticleartifact reductionautomationelectromyographyempirical mode decompositionentropyfeature extractionfemalehandhand griphand movementhumank fold cross validationmalemeasurement accuracymuscle contractionmuscle strengthmyoelectric controlnormal humanradial basis functionsignal processingsupport vector machinewristartifactautomated pattern recognitionlimb prosthesismotionmovement (physiology)physiologyproceduresskeletal muscleAdultAlgorithmsArtifactsArtificial LimbsElectromyographyFemaleHandHumansMaleMotionMovementMuscle ContractionMuscle, SkeletalPattern Recognition, AutomatedSignal Processing, Computer-Assisted