Neural Process Lett https://doi.org/10.1007/s11063-018-9862-0 Feature Extraction and Classiﬁcation of Hand Movements Surface Electromyogram Signals Based on Multi-method Integration 1 1 1 Li Ge · Li-Juan Ge · Jing Hu © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract On the basis of analysing the characteristics of hand movement surface electrocar- diogram electromyogram (sEMG) signals, we propose a feature extraction and classiﬁcation method for hand movement sEMG signals based on a multi-method integration combin- ing the wavelet, fractal and statistics methods. To start, the hand movement sEMG signals are de-noised by using the wavelet transform, the de-noised and reconstructed signals are decomposed, and the average high frequency coefﬁcients in each scale space are calculated to constitute the feature vectors as the ﬁrst part of the hand movement sEMG signals classiﬁca- tion features. Next, according to the characteristics of hand movement sEMG signals and the classiﬁcation needs, we analyse the multi-fractal spectrum of the de-noised and reconstructed signals at multiple scales and extract the relevant parameters of multi-fractal spectrum as the second part of the hand movement sEMG signals classiﬁcation features. Then, according to the characteristics of hand movement sEMG signals, we extract the relevant statistical characteristics of sEMG
Neural Processing Letters – Springer Journals
Published: May 28, 2018
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