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dc.contributor.authorSubasi, Abdulhamit
dc.contributor.authorMian Qaisar, Saeed
dc.date.accessioned2023-03-12T13:30:22Z
dc.date.available2023-03-12T13:30:22Z
dc.date.issued2020-04-25
dc.identifier.citationSubasi, A., Qaisar, S.M. Surface EMG signal classification using TQWT, Bagging and Boosting for hand movement recognition. J Ambient Intell Human Comput 13, 3539–3554 (2022). https://doi.org/10.1007/s12652-020-01980-6en_US
dc.identifier.doihttps://doi.org/10.1007/s12652-020-01980-6en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/581
dc.description.abstractHands play a significant role in grasping and manipulating different objects. The loss of even a single hand have impact on the human activity. In this regard, a prosthetic hand is an appealing solution for the subjects who lost their hands. The surface electromyogram (sEMG) plays a vital role in the design of prosthesis hands. The ensemble classifiers achieve better performance by using a weighted combination of several classifier models. Hence, in this paper, the feasibility of the Bagging and the Boosting ensemble classifiers is assessed for the basic hand movement recognition by using sEMG signals, which were recorded during the grasping movements with various objects for the six hand motions. So, the novelty of the current study is the development of an ensemble model for hand movement recognition based on the tunable Q-factor wavelet transform (TQWT). The proposed method consists of three steps. In the first step, MSPCA is used for denoising. In the second step, a novel feature extraction method, TQWT is used for feature extraction from the sEMG signals, then, statistical values of TQWT sub-bands are calculated. In the last step, the obtained feature set is used as input to an ensemble classifier for the identification of intended hand movements. Performances of the Bagging and the Boosting ensemble classifiers are compared in terms of different performance measures. Using TQWT extracted features along with the presented the Adaboost with SVM and the Multiboost with SVM classifier results in a classification accuracy up to 100%. Hence, the results have shown that the proposed framework has achieved overall better performance and it is a potential candidate for the prosthetic hands control.en_US
dc.subjectProsthetic Hand Controlen_US
dc.subjectSurface Electromyography (sEMG)en_US
dc.subjectMulti-Scale Principle Component Analysis (MSPCA)en_US
dc.subjectTunable Q Wavelet Transform (TQWT)en_US
dc.subjectEnsemble Classifiersen_US
dc.subjectBaggingen_US
dc.subjectBoostingen_US
dc.titleSurface EMG signal classification using TQWT, Bagging and Boosting for hand movement recognitionen_US
dc.source.journalJournal of Ambient Intelligence and Humanized Computingen_US
dc.contributor.researcherCollege collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSAHEALTHen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorSubasi, Abdulhamit


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