%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Kuzborskij_EMBC_2012,
         author = {Kuzborskij, Ilja and Gijsberts, Arjan and Caputo, Barbara},
       projects = {Idiap, NINAPRO},
          title = {On the Challenge of Classifying 52 Hand Movements from Surface Electromyography},
      booktitle = {34th Annual Conference of the IEEE Engineering in Medicine & Biology Society},
           year = {2012},
       abstract = {The level of dexterity of myoelectric hand prostheses
depends to large extent on the feature representation
and subsequent classification of surface electromyography signals.
This work presents a comparison of various feature
extraction and classification methods on a large-scale surface
electromyography database containing 52 different hand movements
obtained from 27 subjects. Results indicate that simple
feature representations as Mean Absolute Value and Waveform
Length can achieve similar performance to the computationally
more demanding marginal Discrete Wavelet Transform. With
respect to classifiers, the Support Vector Machine was found to
be the only method that consistently achieved top performance
in combination with each feature extraction method.},
            pdf = {https://publications.idiap.ch/attachments/papers/2012/Kuzborskij_EMBC_2012.pdf}
}