Unstable Morse Code recognition system with back propagation neural network for person with disabilities.
D. Fuh, and C. Luo. J Med Eng Technol, 25 (3):
118--123(2001)
Abstract
A Morse code auto-recognition system is limited by stable typing speed and stable typing ratio from long to short intervals. For an unstable Morse code typing pattern, the auto-recognition algorithms in the literature are not good enough for applications. This paper adopted a neural network to recognize unstable Morse codes. From an experiment on a teenager with cerebral palsy, the neural network has an average recognition rate up to 93.2\%. The recognition rate from an amputee aged 40, who used a prosthesis for typing, it is 97.2\% on average. When we compare this to 99.2\% for the recognition rate from a skilled expert, the result is quite promising. The neural network has successfully overcome the difficulty of analysing a severely unstable Morse code time series. Since the human typing speed is quite slow in comparison to signal processing by the computer, it also makes it possible to use a neural network for real-time signal recognition.
%0 Journal Article
%1 Fuh2001
%A Fuh, D. T.
%A Luo, C. H.
%D 2001
%J J Med Eng Technol
%K Adolescent; Adult; Amputees; Cerebral Palsy; Communication Aids for Disabled; Computers; Humans; Learning; Neural Networks (Computer); Pattern Recognition, Automated
%N 3
%P 118--123
%T Unstable Morse Code recognition system with back propagation neural network for person with disabilities.
%V 25
%X A Morse code auto-recognition system is limited by stable typing speed and stable typing ratio from long to short intervals. For an unstable Morse code typing pattern, the auto-recognition algorithms in the literature are not good enough for applications. This paper adopted a neural network to recognize unstable Morse codes. From an experiment on a teenager with cerebral palsy, the neural network has an average recognition rate up to 93.2\%. The recognition rate from an amputee aged 40, who used a prosthesis for typing, it is 97.2\% on average. When we compare this to 99.2\% for the recognition rate from a skilled expert, the result is quite promising. The neural network has successfully overcome the difficulty of analysing a severely unstable Morse code time series. Since the human typing speed is quite slow in comparison to signal processing by the computer, it also makes it possible to use a neural network for real-time signal recognition.
@article{Fuh2001,
abstract = {A Morse code auto-recognition system is limited by stable typing speed and stable typing ratio from long to short intervals. For an unstable Morse code typing pattern, the auto-recognition algorithms in the literature are not good enough for applications. This paper adopted a neural network to recognize unstable Morse codes. From an experiment on a teenager with cerebral palsy, the neural network has an average recognition rate up to 93.2\%. The recognition rate from an amputee aged 40, who used a prosthesis for typing, it is 97.2\% on average. When we compare this to 99.2\% for the recognition rate from a skilled expert, the result is quite promising. The neural network has successfully overcome the difficulty of analysing a severely unstable Morse code time series. Since the human typing speed is quite slow in comparison to signal processing by the computer, it also makes it possible to use a neural network for real-time signal recognition.},
added-at = {2014-07-19T19:29:13.000+0200},
author = {Fuh, D. T. and Luo, C. H.},
biburl = {https://www.bibsonomy.org/bibtex/272558363548906a1613b37e37d780e77/ar0berts},
groups = {public},
interhash = {670bfbaf12c9730744292aa87d0ddd83},
intrahash = {72558363548906a1613b37e37d780e77},
journal = {J Med Eng Technol},
keywords = {Adolescent; Adult; Amputees; Cerebral Palsy; Communication Aids for Disabled; Computers; Humans; Learning; Neural Networks (Computer); Pattern Recognition, Automated},
number = 3,
pages = {118--123},
pmid = {11530826},
timestamp = {2014-07-19T19:29:13.000+0200},
title = {Unstable Morse Code recognition system with back propagation neural network for person with disabilities.},
username = {ar0berts},
volume = 25,
year = 2001
}