Hilbert–Huang Transform Applied to the Recognition of Multimodal Biosignals in the Control of Bioprosthetic Hand
This paper deals with the problem of bioprosthetic hand control via recognition of user intent on the basis of electromyography (EMG) and mechanomyography (MMG) signals acquired from the surface of a forearm. As a method of signal parameterization the Hilbert–Huang (HH) transform is applied which is an effective tool for reduction of feature space dimension. The performance of proposed recognition method based on HH transform of EMG and MMG signals was experimentally compared against an autoregressive model of dimensionality reduction using real data concerning the recognition of five types of grasping movements. The experimental results show that the HH transform approach with root mean square of amplitude feature outperforms an autoregressive method.
This work was financed from the National Science Center resources in 2012–2014 years as a research project No ST6/06168 and supported by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Technology.
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