Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/35345
Título: Myoelectronic signal-based methodology for the analysis of handwritten signatures
Autores/as: Carmona Duarte, María Cristina 
Sanchez De Torres Peralta, Rafael 
Diaz Cabrera, Moises 
Ferrer Ballester, Miguel Ángel 
Martin Rincon, Marcos 
Clasificación UNESCO: 3314 Tecnología médica
Palabras clave: Handwritten signatures
EMG
Bio-medical signal processing
Fecha de publicación: 2017
Proyectos: Generacion de Un Marco Unificado Para El Desarrollo de Patrones Biometricos de Comportamiento 
Publicación seriada: Human Movement Science 
Resumen: 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
Fuente: Human Movement Science [ISSN 0167-9457], v. 55, p. 18-30
Colección:Artículos
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