Abstract
This paper introduced a novel, simple and ef-fective method to extract the general feature of two surface EMG (electromyography) signal patterns: forearm supination (FS) surface EMG signal and forearm pronation (FP) surface EMG signal. After surface EMG (SEMG) signal was decomposed to the fourth resolution level with wavelet packet transform (WPT), its whole scaling space (with frequencies in the interval (0Hz, 500Hz]) was divided into16 frequency bands (FB). Then wavelet coefficient entropy (WCE) of every FB was calculated and corre-spondingly marked with WCE(n) (from the nth FB, n=1,2,…16). Lastly, some WCE(n) were chosen to form WCE feature vector, which was used to distinguish FS surface EMG signals from FP surface EMG signals. The result showed that the WCE feather vector consisted of WCE(7) (187.25Hz, 218.75Hz) and WCE(8) (218.75Hz, 250Hz) can more effectively recog-nize FS and FP patterns than other WCE feature vector or the WPT feature vector which was gained by the combination of WPT and principal components analysis.
Highlights
Due to its noninvasive measurement, surface EMG (SEMG) signal has been widely applied in many fields [1,2,3]
The SEMG signal recorded from the skin surface over limb muscles in the process of the limb actions is called action SEMG (ASEMG) signal
ASEMG signal is constituted by many motor unit action potentials (MUAPs) from many recruited motor units under surface electrode and noise [7]
Summary
Due to its noninvasive measurement, surface EMG (SEMG) signal has been widely applied in many fields [1,2,3]. Containing the electrical and functional properties of limb muscle contraction [4] and providing the information about the neuromuscular activity from which ASEMG signal originates [5], ASEMG signal has been widely researched and used in rehabilitation and the controls of prosthetic devices for individuals with amputations or congenitally deficient limbs [6]. Due to its noninvasive measurement, there was still an obvious motivation to explore some more effective algorithms to extract the features from shorter surface EMG signal and to reduce the error identification rate. The following paragraphs were firstly to explain the scheme of acquiring surface EMG signal; to introduce the method of calculating WCE feature vector and recognizing FS and FP pattern; lastly to analyze and discuss the research results
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