%0 Journal Article
%T Recognition of Handwriting from Electromyography
%A Michael Linderman
%A Mikhail A. Lebedev
%A Joseph S. Erlichman
%J PLOS ONE
%D 2009
%I Public Library of Science (PLoS)
%R 10.1371/journal.pone.0006791
%X Handwriting ¨C one of the most important developments in human culture ¨C 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 ¨C 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.
%U http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0006791