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  • 标题:Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals
  • 本地全文:下载
  • 作者:Ines Chihi ; Lilia Sidhom ; Ernest Nlandu Kamavuako
  • 期刊名称:Biosensors
  • 电子版ISSN:2079-6374
  • 出版年度:2022
  • 卷号:12
  • 期号:2
  • DOI:10.3390/bios12020117
  • 语种:English
  • 出版社:MDPI Publishing
  • 摘要:This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on the nonlinear Hammerstein–Wiener model, the first part of this study outlines the estimation of different sub-models to mimic diverse force profiles. The second part fixes the appropriate sub-models of a multimodel library and computes the contribution of sub-models to estimate the desired force. Based on a pre-existing dataset, the obtained results show the effectiveness of the proposed approach to estimate muscle force from EMG signals with reasonable accuracy. The coefficient of determination ranges from 0.6568 to 0.9754 using the proposed method compared with a range of 0.5060 to 0.9329 using an artificial neural network (ANN), generating significantly different accuracy ( p < 0.03). Results imply that the use of multimodel approach can improve the accuracy in proportional control of prostheses.
  • 关键词:enelectromyography (EMG) signalsHammerstein–Wiener modelmultimodelartificial neural networkmuscle force
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