EMG-based facial gesture recognition through versatile elliptic basis function neural network

@inproceedings{Hamedi2013EMGbasedFG,
  title={EMG-based facial gesture recognition through versatile elliptic basis function neural network},
  author={Mahyar Hamedi and Sheikh Hussain Shaikh Salleh and Mehdi Astaraki and Alias Mohd Noor},
  booktitle={Biomedical engineering online},
  year={2013}
}
BackgroundRecently, the recognition of different facial gestures using facial neuromuscular activities has been proposed for human machine interfacing applications. Facial electromyograms (EMGs) analysis is a complicated field in biomedical signal processing where accuracy and low computational cost are significant concerns. In this paper, a very fast versatile elliptic basis function neural network (VEBFNN) was proposed to classify different facial gestures. The effectiveness of different… CONTINUE READING
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Key Quantitative Results

  • ResultsThe average classification results showed that the best performance for recognizing facial gestures among all single/multi-features was achieved by Maximum Peak Value with 87.1% accuracy.
  • The average classification results showed that the best performance for recognizing facial gestures among all single/multi-features was achieved by Maximum Peak Value with 87.1% accuracy.

Citations

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EMG-based Real Time Facial Gesture Recognition for Stress Monitoring

  • 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • 2018
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Emotional state detection based on EMG and EOG biosignals: A short survey

  • 2017 IEEE 5th Portuguese Meeting on Bioengineering (ENBENG)
  • 2017
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