Unstable Morse Code recognition system with back propagation neural network for person with disabilities.
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.
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Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, ROC.
Luo
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CH
D000293:Adolescent; D000328:Adult; D000674:Amputees; D002547:Cerebral Palsy; D003143:Communication Devices for People with Disabilities; D003201:Computers; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated