Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Improvement of Classification Accuracy of Different Finger Movements Using Surface Electromyography Based on Long Short-Term Memory

LSTM을 이용한 표면 근전도 분석을 통한 서로 다른 손가락 움직임 분류 정확도 향상

  • Shin, Jaeyoung (Department of Electrical Engineering, Wonkwang University) ;
  • Kim, Seong-Uk (Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Lee, Yun-Sung (Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Lee, Hyung-Tak (Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Hwang, Han-Jeong (Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology)
  • 신재영 (원광대학교 전자공학과) ;
  • 김성욱 (금오공과대학교 메디컬IT융합공학과) ;
  • 이윤성 (금오공과대학교 메디컬IT융합공학과) ;
  • 이형탁 (금오공과대학교 메디컬IT융합공학과) ;
  • 황한정 (금오공과대학교 메디컬IT융합공학과)
  • Received : 2019.10.08
  • Accepted : 2019.12.09
  • Published : 2019.12.31
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Abstract

Forearm electromyography (EMG) generated by wrist movements has been widely used to develop an electrical prosthetic hand, but EMG generated by finger movements has been rarely used even though 20% of amputees lose fingers. The goal of this study is to improve the classification performance of different finger movements using a deep learning algorithm, and thereby contributing to the development of a high-performance finger-based prosthetic hand. Ten participants took part in this study, and they performed seven different finger movements forty times each (thumb, index, middle, ring, little, fist and rest) during which EMG was measured from the back of the right hand using four bipolar electrodes. We extracted mean absolute value (MAV), root mean square (RMS), and mean (MEAN) from the measured EMGs for each trial as features, and a 5x5-fold cross-validation was performed to estimate the classification performance of seven different finger movements. A long short-term memory (LSTM) model was used as a classifier, and linear discriminant analysis (LDA) that is a widely used classifier in previous studies was also used for comparison. The best performance of the LSTM model (sensitivity: 91.46 ± 6.72%; specificity: 91.27 ± 4.18%; accuracy: 91.26 ± 4.09%) significantly outperformed that of LDA (sensitivity: 84.55 ± 9.61%; specificity: 84.02 ± 6.00%; accuracy: 84.00 ± 5.87%). Our result demonstrates the feasibility of a deep learning algorithm (LSTM) to improve the performance of classifying different finger movements using EMG.

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