A Consistency Study of CNN's Learning to Recognize Korean Finger Number using sEMG Signals

표면근전도 신호를 활용한 한국 숫자지화 인식에서 CNN 학습의 일관성에 관한 연구

  • Park, Jong-Jun (Department of Medical IT Engineering, Soonchunhyang University) ;
  • Kwon, Chun-Ki (Department of Medical IT Engineering, Soonchunhyang University)
  • 박종준 (순천향대학교 의료IT공학과) ;
  • 권춘기 (순천향대학교 의료IT공학과)
  • Received : 2018.07.25
  • Accepted : 2018.10.05
  • Published : 2018.10.31


Convolutional Neural Network (CNN) has been actively employed in the application of computer vision, and has been proved to have its superior performance in image classification, document classification, and finger print recognition. This work focuses on an application of CNN, having outstanding performance in image classification, to recognition of korean finger number using time series sEMG signals as input and validates CNN's capability in providing its consistent learning in repeated application for recognition of sEMG based Korean finger numbers, which has been rarely a topic in previous studies. To this end, 252 sEMG signals as input data and 108 sEMG signals as test data out of 360 sEMG signals (60 signals each number) acquired from a forearm muscle of the subject who is trained to consistently perform six Korean finger number gestures from zero(0) to five(5) were used for CNN based finger number recognition. CNN was set to have 100 learning iterations for each application of finger number recognition, and to have 10 repetitive applications of finger number recognition for the consistency of CNN's learning. Recognition rate at each repetition was calculated from test data. As can be seen from the results in this work, CNN shows consistent learning at each repetitive application of finger number recognition and outstanding recognition rates of more than 99.1% (missed one case out of 60 cases). Thus, CNN is one of powerful techniques for finger number recognition based on time-series sEMG signals to provide not only global solution but also excellent recognition rates.


convolutional neural network;time-series signal;surface electromyography;Korean finger number gesture recognition;consistency in cnn learning;repeated recognition application


Supported by : 순천향대학교


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