Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/35345
Title: Myoelectronic signal-based methodology for the analysis of handwritten signatures
Authors: Carmona Duarte, María Cristina 
Sanchez De Torres Peralta, Rafael 
Diaz Cabrera, Moises 
Ferrer Ballester, Miguel Ángel 
Martin Rincon, Marcos 
UNESCO Clasification: 3314 Tecnología médica
Keywords: Handwritten signatures
EMG
Bio-medical signal processing
Issue Date: 2017
Project: Generacion de Un Marco Unificado Para El Desarrollo de Patrones Biometricos de Comportamiento 
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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