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Hum Mov Sci. 2017 Oct;55:18-30. doi: 10.1016/j.humov.2017.07.002. Epub 2017 Jul 24.

Myoelectronic signal-based methodology for the analysis of handwritten signatures.

Author information

1
Instituto Universitario para el Desarrollo Tecnológico y la Innovación en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain. Electronic address: ccarmona@idetic.eu.
2
Department of Physical Education, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain. Electronic address: rafaelsanchezdetorres@gmail.com.
3
Instituto Universitario para el Desarrollo Tecnológico y la Innovación en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain. Electronic address: mdiaz@idetic.eu.
4
Instituto Universitario para el Desarrollo Tecnológico y la Innovación en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain. Electronic address: mferrer@idetic.eu.
5
Department of Physical Education, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain. Electronic address: marcos.martinrincon@gmail.com.

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.

KEYWORDS:

Bio-medical signal processing; EMG; Handwritten signatures

PMID:
28750258
DOI:
10.1016/j.humov.2017.07.002
[Indexed for MEDLINE]

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