:Surface electromyogram(sEMG)processing and classication can assist neurophysiological standardization and evaluation and provide habitational detection.The timing of muscle activation is critical in determining vario...:Surface electromyogram(sEMG)processing and classication can assist neurophysiological standardization and evaluation and provide habitational detection.The timing of muscle activation is critical in determining various medical conditions when looking at sEMG signals.Understanding muscle activation timing allows identication of muscle locations and feature validation for precise modeling.This work aims to develop a predictive model to investigate and interpret Patellofemoral(PF)osteoarthritis based on features extracted from the sEMG signal using pattern classication.To this end,sEMG signals were acquired from ve core muscles over about 200 reads from healthy adult patients while they were going upstairs.Onset,offset,and time duration for the Transversus Abdominus(TrA),Vastus Medialis Obliquus(VMO),Gluteus Medius(GM),Vastus Lateralis(VL),and Multidus Muscles(ML)were acquired to construct a classication model.The proposed classication model investigates function mapping from real-time space to a PF osteoarthritis discriminative feature space.The activation feature space of muscle timing is used to train several large margin classiers to modulate muscle activations and account for such activation measurements.The fast large margin classier achieved higher performance and faster convergence than support vector machines(SVMs)and other state-of-the-art classiers.The proposed sEMG classication framework achieved an average accuracy of 98.8%after 7 s training time,improving other classication techniques in previous literature.展开更多
基金work was supported by the National Research Foundation of Korea(NRF)Grant funded by the Korean government(MSIT)(NRF-2016R1D1A1A03934816)and by Chowis。
文摘:Surface electromyogram(sEMG)processing and classication can assist neurophysiological standardization and evaluation and provide habitational detection.The timing of muscle activation is critical in determining various medical conditions when looking at sEMG signals.Understanding muscle activation timing allows identication of muscle locations and feature validation for precise modeling.This work aims to develop a predictive model to investigate and interpret Patellofemoral(PF)osteoarthritis based on features extracted from the sEMG signal using pattern classication.To this end,sEMG signals were acquired from ve core muscles over about 200 reads from healthy adult patients while they were going upstairs.Onset,offset,and time duration for the Transversus Abdominus(TrA),Vastus Medialis Obliquus(VMO),Gluteus Medius(GM),Vastus Lateralis(VL),and Multidus Muscles(ML)were acquired to construct a classication model.The proposed classication model investigates function mapping from real-time space to a PF osteoarthritis discriminative feature space.The activation feature space of muscle timing is used to train several large margin classiers to modulate muscle activations and account for such activation measurements.The fast large margin classier achieved higher performance and faster convergence than support vector machines(SVMs)and other state-of-the-art classiers.The proposed sEMG classication framework achieved an average accuracy of 98.8%after 7 s training time,improving other classication techniques in previous literature.
