Poster Open Access

Pattern recognition myoelectric control: Evaluating EMG pattern separability

Franzke, AW; Kristoffersen, MB, Murgia, A; Sluis, CK van der; Bongers, RM,


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        <foaf:name>Franzke, AW; Kristoffersen, MB, Murgia, A; Sluis, CK van der; Bongers, RM,</foaf:name>
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        <foaf:familyName>Franzke</foaf:familyName>
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    <dct:title>Pattern recognition myoelectric control: Evaluating EMG pattern separability</dct:title>
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    <dcat:keyword>machine learning control</dcat:keyword>
    <dcat:keyword>pattern recognition control</dcat:keyword>
    <dcat:keyword>emg pattern</dcat:keyword>
    <dcat:keyword>myoelectric control</dcat:keyword>
    <dcat:keyword>upper limb myoelectric prostheses</dcat:keyword>
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    <dct:description>&lt;p&gt;Pattern recognition based myoelectric control for upper limb prostheses has gained increasing attention in the last years as it seems to offer more intuitive control than conventional, direct control. However, for such control to be feasible, the prosthesis user needs to have sufficient control of muscle contractions to create EMG patterns suitable for pattern classification. Few studies have investigated the relation between control ability and EMG pattern characteristics and an evaluation tool for quality of EMG patterns is still missing. We proposed such a tool and investigated whether scores from this tool were related to EMG pattern control ability.&lt;/p&gt; &lt;p&gt;This poster was presented during the 2nd international symposium on innovations in amputations surgery and prosthetic technologies in Vienna, May 10-12, 2018.&lt;/p&gt;</dct:description>
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    <dct:title>Intuitive Natural Prosthesis UTilization</dct:title>
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