@InProceedings{Samadani2014,
  Title                    = {Hand gesture recognition based on surface electromyography},
  Author                   = {Samadani, A. and Kuli\'{c}, D.},
  Booktitle                = {Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society},
  Year                     = {2014},
  Pages                    = {4196--9},

  Abstract                 = {Human hands are the most dexterous of human limbs and hand gestures play an important role in non-verbal communication. Underlying electromyograms associated with hand gestures provide a wealth of information based on which varying hand gestures can be recognized. This paper develops an inter-individual hand gesture recognition model based on Hidden Markov models that receives surface electromyography (sEMG) signals as inputs and predicts a corresponding hand gesture. The developed recognition model is tested with a dataset of 10 various hand gestures performed by 25 subjects in a leave-one-subject-out cross validation and an inter-individual recognition rate of 79% was achieved. The promising recognition rate demonstrates the efficacy of the proposed approach for discriminating between gesture-specific sEMG signals and could inform the design of sEMG-controlled prostheses and assistive devices.},
  Owner                    = {jf2lin},
  Timestamp                = {2015.05.21}
}

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