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L. Hargrove, K. Englehart and B. Hudgins, “A Training Strategy to Reduce Classification Degradation Due to Electrode Displacements in Pattern Recognition Based Myoelectric Control,” Biomedical Signal Processing and Control, Vol. 3, No. 2, 2008, pp. 175-180. doi:10.1016/j.bspc.2007.11.005

has been cited by the following article:

• TITLE: Online Finger Gesture Recognition Using Surface Electromyography Signals

  AUTHORS: Qiang Li, Bo Li

  KEYWORDS: Finger Gesture; sEMG Signal; Online; Recognition

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.4 No.2, May 8, 2013

  ABSTRACT: The analysis on the online finger gesture recognition using multi-channel sEMG signals was explored in this paper. Nine types of gestures were applied to be identified, involving six kinds of numerical finger gestures and three kinds of hand gestures. The time domain parameters were extracted to be the features. And then, the probabilistic neural network was utilized to classify the proposed gestures with the extracted features. The experimental results showed that most of gestures could acquire the acceptable classification performance and a few elaborate gestures were hard to acquire the effective identification.