Study on Forearm Muscles and Electrode Placements for CNN based Korean Finger Number Gesture Recognition using sEMG Signals

표면근전도 신호를 활용한 CNN 기반 한국 지화숫자 인식을 위한 아래팔 근육과 전극 위치에 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 : 2018.05.15
  • Accepted : 2018.08.03
  • Published : 2018.08.31


Surface electromyography (sEMG) is mainly used as an on/off switch in the early stage of the study and was then expanded to navigational control of powered-wheelchairs and recognition of sign language or finger gestures. There are difficulties in communication between people who know and do not know sign language; therefore, many efforts have been made to recognize sign language or finger gestures. Recently, use of sEMG signals to recognize sign language signals have been investigated; however, most studies of this topic conducted to date have focused on Chinese finger number gestures. Since sign language and finger gestures vary among regions, Korean- and Chinese-finger number gestures differ from each other. Accordingly, the recognition performance of Korean finger number gestures based on sEMG signals can be severely degraded if the same muscles are specified as for Chinese finger number gestures. However, few studies of Korean finger number gestures based on sEMG signals have been conducted. Thus, this study was conducted to identify potential forearm muscles from which to collect sEMG signals for Korean finger number gestures. To accomplish this, six Korean finger number gestures from number zero to five were investigated to determine the usefulness of the proposed muscles and electrode placements by showing that CNN technique based on sEMG signal after sufficient learning recognizes six Korean finger number gestures in accuracy of 100%.


surface electromyography;forearm;multi finger gesture recognition;electrode placements;Korean multi finger number


Supported by : 순천향대학교


  1. H. S. Kang, "Finger Sign Recognition Technique using sEMG Sensor and Gyro Sensor", Master Thesis, Soongsil University, 2002.
  2. A. Phinyomark, P. Phukpattaranont, C. Limsakul, "A review of Control Methods for Electric Power Wheelchairs Based on Electromyography Signals with Special Emphasis on Pattern Recognition", IETE Technical Review, Vol.28, No.4, pp.316-326, 2011. DOI:
  3. A. J. Young, L. J. Hargrove, T. A. Kuiken, "Improving Myoelectric Pattern Recognition Robustness to Electrode Shift by Changing Interelectrode Distance and Electrode Configuration", IEEE Transactions on Biomedical Engineering, Vol.59, No.3, pp.645-652, 2012. DOI:
  4. F. Tenore, A. Ramos, A. Fahmy, S. Acharya, R. Etienne-Cummings, N. V. Thakor, "Towards the Control of Individual Fingers of a Prosthetic Hand Using Surface EMG Signals", Proceedings of 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.6145-6148, 2007. DOI:
  5. L. Hargrove, K. Englehart B. Hudgins, "A comparison of Surface and Intramuscular Myoelectric Signal Classification", IEEE Transactions on Biomedical Engineering, Vol.54, No.5, pp.847-853, 2007. DOI:
  6. A. J. Andrews, "Finger Movement Classification using Forearm EMG Signals", Thesis of Master's Degree, Queen's University, 2008.
  7. J. M. Hahne, B. Graimann, K. R. Muller, "Spatial Filtering for Robust Myoelectric Control", IEEE Transactions on Biomedical Engineering, Vol.59, No.5, pp.1436-1443, 2012. DOI:
  8. L. Pan, D. Zhang, N. Jiang, X. Sheng, X. Zhu, "Improving Robustness against Electrode Shift of High Density EMG for Myoelectric Control throught Common Spatial Patterns", Journal of NeuroEngineering and Rehabilitation, Vol.12, No.110, pp.1-16, 2015. DOI:
  9. X. Chen, Z. J. Wang, "Pattern recognition of number gestures based on a wireless surface EMG system", Biomedical Signal Processing and Control, Vol.8, No.2, pp.184-192, 2013. DOI:
  10. X. Zhang, X. Chen, Y. Li, V. Lantz, K. Wang, J. Yang, "A Framework for Hand Gesture Recognition based on Accelerometer and EMG Sensors", IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, Vol.41, No.6, pp.1064-1076, 2011. DOI:
  11. Jong-Jun Park, "Study on sEMG Signal Based Finger Sign Recognition using Convolutional Neural Network", Thesis of Master's Degree, Soonchunhyang University, 2017.
  12. C. Lee, J. Kim, G. Park, J. Byeon, W. Jang, S. Kim, "Implementation of Real-time Recognition System for Korean Sign Language and Finger Gestures", Proceedings of Korean Institute of Intelligent Systems, Vol.6, No.2, pp.333-336, 1996.
  13. H. D. Yang, S. W. Lee, "Automatic Spotting of Sign and Fingerspelling for Continuous Sign Language Recognition", Journal of KISS : Software and Applications, Vol.38, No.2, pp.102-107, 2011. UCI:
  14. N. H. Kim, "A Development of the Next-generation Interface System Based on the Finger Gesture Recognizing in Use of Image Process Techniques", Journal of the Korea Institute of Information and Communication Engineering, Vol.15, No.4, pp.935-942, 2011. DOI:
  15. J. Lee, Y. Kim, J. Song, K. Han, Y. Hong, "First Step to Korean Sign Language", Nanam Publisher, 2010 ISBN: 978-89-300-8463-5
  16. Faculty Association of Korea Anatomy and Physiology, "Human Anatomy", Hyunmoonsa Publisher, pp.214-218, 2009.