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dc.contributor.authorLi, Rongjie
dc.contributor.authorWu, Yao
dc.contributor.authorWu, Qun
dc.contributor.authorDey, Nilanjan
dc.contributor.authorGonzález-Crespo, Rubén (1)
dc.contributor.authorShi, Fuqian
dc.description.abstractSurface electromyography (sEMG) has been widely used in clinical medicine, rehabilitation medicine, and intelligent robots. Currently, sEMG signal classification methods promoted the development and industrialization of sEMG control bionic prostheses. Emotion recognition using sEMG signal is crucial in human–computer interaction (HCI) and becoming a research hotspot. While the high rate of emotion recognition is still the key issue for the emotion applications. Employing sEMG to study emotion classification can improve the recognition rate and eliminate subjective interference. In this research, the Markov transition field (MTF) method was applied to convert sEMG signals to images; and this crucial converting process makes convolutional neural networks adopting the input resource. A 69-INPUT-6 -OUTPUT primary deep neural network was constructed for classifying the human emotion states under emotion stimuli experiment. The MTF-based deep neural network (MTF-DNN) for classifying sEMG signals was developed and validated subsequently. The result showed that the high effectiveness of the proposed classification model. The proposed MTFDNN performs high efficacy in the indices of classification of Ac (0.9102), Pr (0.1867), and Fm (0.9089) by comparing with different classification models.es_ES
dc.publisherElsevier B.V.es_ES
dc.relation.ispartofseries;vol. 189
dc.subjectdeep neural networkses_ES
dc.subjectMarkov transition fieldes_ES
dc.subjectsignal classificationes_ES
dc.subjectsignal image transitiones_ES
dc.subjectsurface electromyographyes_ES
dc.titleEmotion stimuli-based surface electromyography signal classification employing Markov transition field and deep neural networkses_ES

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