TY - JOUR
T1 - Recognition of handwriting from electromyography
AU - Linderman, Michael
AU - Lebedev, Mikhail A.
AU - Erlichman, Joseph S.
N1 - Funding Information:
Michael Linderman is paid employee and the owner of Norconnect Inc. Michael Linderman is going to develop products from this and future research. He is the author of two pending patents with US Patent Office. Recordation of handwriting and hand movement using electromyography 11640954. Handwriting EMG for Medical Diagnosis 61108603. Michael Linderman has 42% stock ownership in Norconnect Inc. He is also the board chairman at Norconnect Inc. This research was supported by the research grant #0711799 to Norconnect from National Science Foundation. Norconnect also won the second award from National Science Foundation to continue this research.
PY - 2009/8/26
Y1 - 2009/8/26
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/69249216585
U2 - 10.1371/journal.pone.0006791
DO - 10.1371/journal.pone.0006791
M3 - Article
C2 - 19707562
AN - SCOPUS:69249216585
SN - 1932-6203
VL - 4
JO - PLoS ONE
JF - PLoS ONE
IS - 8
M1 - e6791
ER -