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• W4223654260 abstract "In recent years surface electromyography signals-based machine learning models are rapidly establishing. The efficacy of prosthetic arm growth for transhumeral amputees is aided by efficient classifiers. The paper aims to propose a stacking classifier-based classification system for sEMG shoulder movements. It presents the possibility of various shoulder motions classification of transhumeral amputees. To improve the system performance, adaptive threshold method and wavelet transformation have been applied for features extraction. Six different classifiers Support Vector Machines (SVM), Tree, Random Forest (RF), K-Nearest Neighbour (KNN), AdaBoost and Naïve Bayes (NB) are designed to extract the sEMG data classification accuracy. With cross-validation, the accuracy of RF, Tree and Ada Boost is 97%, 92% and 92% respectively. Stacking classifiers provides an accuracy as 99.4% after combining the best predicted multiple classifiers." @default.
• W4223654260 created "2022-04-15" @default.
• W4223654260 creator A5008527378 @default.
• W4223654260 date "2022-04-01" @default.
• W4223654260 modified "2023-09-30" @default.
• W4223654260 title "Stacking classifier to improve the classification of shoulder motion in transhumeral amputees" @default.
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• W4223654260 doi "https://doi.org/10.1515/bmt-2020-0343" @default.
• W4223654260 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35363448" @default.
• W4223654260 hasPublicationYear "2022" @default.
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