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|Title:||Myoelectronic signal-based methodology for the analysis of handwritten signatures||Authors:||Carmona-Duarte, Cristina
de Torres-Peralta, Rafael
Ferrer, Miguel A.
|Keywords:||3314 Tecnología médica||metadata.dc.subject.other:||Handwritten signatures
Bio-medical signal processing
|Issue Date:||2017||Journal:||Human Movement Science||Abstract:||With the overall aim of improving the synthesis of handwritten signatures, we have studied how muscle activation depends on handwriting style for both text and flourish. Surface electromyographic (EMG) signals from a set of twelve arm and trunk muscles were recorded in synchronization with handwriting produced on a digital Tablet. Correlations between these EMG signals and handwritten trajectory signals were analyzed so as to define the sequence of muscles activated during the different parts of the signature. Our results establish a correlation between the speed of the movement, stroke size, handwriting style and muscle activation. Muscle activity appeared to be clustered as a function of movement speed and handwriting style, a finding which may be used for filter design in a signature synthesizer.||URI:||http://hdl.handle.net/10553/35345||ISSN:||0167-9457||DOI:||10.1016/j.humov.2017.07.002||Source:||Human Movement Science[ISSN 0167-9457],v. 55, p. 18-30|
|Appears in Collections:||Artículos|
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