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Recognition of Handwriting from Electromyography

Handwriting – one of the most important developments in human culture – is also a methodological tool in several scientific disciplines, most importantly handwriting recognition methods, graphology and medical diagnostics. Previous studies have relied largely on the analyses of handwritten traces or...

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Detalles Bibliográficos
Autores principales: Linderman, Michael, Lebedev, Mikhail A., Erlichman, Joseph S.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2727961/
https://www.ncbi.nlm.nih.gov/pubmed/19707562
http://dx.doi.org/10.1371/journal.pone.0006791
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author Linderman, Michael
Lebedev, Mikhail A.
Erlichman, Joseph S.
author_facet Linderman, Michael
Lebedev, Mikhail A.
Erlichman, Joseph S.
author_sort Linderman, Michael
collection PubMed
description Handwriting – one of the most important developments in human culture – is also a methodological tool in several scientific disciplines, most importantly handwriting recognition methods, graphology and medical diagnostics. Previous studies have relied largely on the analyses of handwritten traces or kinematic analysis of handwriting; whereas electromyographic (EMG) signals associated with handwriting have received little attention. Here we show for the first time, a method in which EMG signals generated by hand and forearm muscles during handwriting activity are reliably translated into both algorithm-generated handwriting traces and font characters using decoding algorithms. Our results demonstrate the feasibility of recreating handwriting solely from EMG signals – the finding that can be utilized in computer peripherals and myoelectric prosthetic devices. Moreover, this approach may provide a rapid and sensitive method for diagnosing a variety of neurogenerative diseases before other symptoms become clear.
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spelling pubmed-27279612009-08-26 Recognition of Handwriting from Electromyography Linderman, Michael Lebedev, Mikhail A. Erlichman, Joseph S. PLoS One Research Article Handwriting – one of the most important developments in human culture – is also a methodological tool in several scientific disciplines, most importantly handwriting recognition methods, graphology and medical diagnostics. Previous studies have relied largely on the analyses of handwritten traces or kinematic analysis of handwriting; whereas electromyographic (EMG) signals associated with handwriting have received little attention. Here we show for the first time, a method in which EMG signals generated by hand and forearm muscles during handwriting activity are reliably translated into both algorithm-generated handwriting traces and font characters using decoding algorithms. Our results demonstrate the feasibility of recreating handwriting solely from EMG signals – the finding that can be utilized in computer peripherals and myoelectric prosthetic devices. Moreover, this approach may provide a rapid and sensitive method for diagnosing a variety of neurogenerative diseases before other symptoms become clear. Public Library of Science 2009-08-26 /pmc/articles/PMC2727961/ /pubmed/19707562 http://dx.doi.org/10.1371/journal.pone.0006791 Text en Linderman et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Linderman, Michael
Lebedev, Mikhail A.
Erlichman, Joseph S.
Recognition of Handwriting from Electromyography
title Recognition of Handwriting from Electromyography
title_full Recognition of Handwriting from Electromyography
title_fullStr Recognition of Handwriting from Electromyography
title_full_unstemmed Recognition of Handwriting from Electromyography
title_short Recognition of Handwriting from Electromyography
title_sort recognition of handwriting from electromyography
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2727961/
https://www.ncbi.nlm.nih.gov/pubmed/19707562
http://dx.doi.org/10.1371/journal.pone.0006791
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AT lebedevmikhaila recognitionofhandwritingfromelectromyography
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