This paper presents the implementation of a system to classify muscular intent. A neural network is used for this purpose. After skin preparation, feature extraction, network training and real-time testing, an average overall classification accuracy of 93.3% over three possible gestures was obtained. Ultimately, the results obtained speak to the suitability of an Arduino-based system for the acquisition and decoding of muscular intent. This result is indicative of the potential of the Arduino microcontroller in this application, to provide effective performance at a far lower price-point than its competition.


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    Titel :

    Cost-effective system for the classification of muscular intent using surface electromyography and artificial neural networks


    Beteiligte:


    Erscheinungsdatum :

    2017-04-01


    Format / Umfang :

    595912 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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