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Wearing-independent hand gesture recognition method based on EMG armband


Electromyographic (EMG) armband with electrodes mounted around the user’s forearm is one of the most ergonomic wearable EMG devices and is used to recognize fine hand gesture with great popularity. Definitely, the distributions of signal differ greatly in different wearing positions of armband based on the physiological characters of EMG, which will cause the performance decline and even the inapplicability of the recognition model built in one position. Hence, this paper proposes a wearing-independent hand gesture recognition method based on EMG armband. To eliminate the influence of wearing position, Standard Space is proposed in this paper. Based on the sequential features of EMG in different scales, the wearing position of armband is predicted and helps unify the original features to the proposed space. Then, with the unified signals, fine hand gesture can be recognized accurately and robustly with lightweight Random Forest (RF). The experimental results showed that the recognition accuracy of the proposed method was 91.47% approximately. And compared with the method without fine feature extraction and feature space unification, the performance was improved by 10.12%.

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  1. Ahsan MR, Ibrahimy MI, Khalifa OO (2009) EMG signal classification for human computer interaction: a review. Eur J Sci Res 33(3):480–501

    Google Scholar 

  2. Song J, Sörös G, Pece F et al. (2015) Real-time hand gesture recognition on unmodified wearable devices. In: IEEE Conference on computer vision and pattern recognition

  3. Ducloux J, Colla P, Petrashin P et al. (2014) Accelerometer-based hand gesture recognition system for interaction in digital TV. In: 2014 IEEE International on instrumentation and measurement technology conference (I2MTC) proceedings. IEEE, pp 1537–1542

  4. Nandakumar R, Iyer V, Tan D et al. (2016) FingerIO: using active sonar for fine-grained finger tracking. In: Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, pp 1515–1525

  5. Lien J, Gillian N, Karagozler ME et al. (2016) Soli: ubiquitous gesture sensing with millimeter wave radar. ACM Trans Graph (TOG) 35(4):142

    Article  Google Scholar 

  6. McIntosh J, Marzo A, Fraser M et al. (2017) EchoFlex: hand gesture recognition using ultrasound imaging. In: Proceedings of the 2017 CHI conference on human factors in computing systems. ACM, pp 1923–1934

  7. Zhang X, Chen X, Li Y et al. (2011) A framework for hand gesture recognition based on accelerometer and EMG sensors. IEEE Trans Syst Man Cybern-Part A: Syst Humans 41(6):1064–1076

    Article  Google Scholar 

  8. Zhang X, Chen X, Wang W et al. (2009) Hand gesture recognition and virtual game control based on 3D accelerometer and EMG sensors. In: Proceedings of the 14th international conference on intelligent user interfaces. ACM, pp 401–406

  9. McIntosh J, McNeill C, Fraser M et al. (2016) EMPress: practical hand gesture classification with wrist-mounted EMG and pressure sensing. In: Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, pp 2332– 2342

  10. Benatti S, Casamassima F, Milosevic B et al. (2015) A versatile embedded platform for EMG acquisition and gesture recognition. IEEE Trans Biomed Circ Syst 9(5):620–630

    Article  Google Scholar 

  11. Myo arm band [Online]. Available:

  12. gForce arm band [Online]. Available:

  13. DTing arm band [Online]. Available:

  14. DTing arm band [Online]. Available:

  15. Mesa I, Rubio A, Tubia I et al. (2014) Channel and feature selection for a surface electromyographic pattern recognition task. Expert Syst Appl 41(11):5190–5200

    Article  Google Scholar 

  16. Castellini C, Fiorilla AE, Sandini G (2009) Multi-subject/daily-life activity EMG-based control of mechanical hands. J Neuroengi Rehab 6(1):41

    Article  Google Scholar 

  17. Kim J, Mastnik S, André E (2008) EMG-based hand gesture recognition for realtime biosignal interfacing. In: Proceedings of the 13th international conference on intelligent user interfaces. ACM, pp 30–39

  18. Assad C, Wolf M, Theodoridis T et al. (2013) Biosleeve: a natural EMG-based interface for HRI. In: Proceedings of the 8th ACM/IEEE international conference on human-robot interaction. IEEE Press, pp 69–70

  19. Samadani AA, Kulic D (2014) Hand gesture recognition based on surface electromyography. In: 2014 36th Annual International conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 4196–4199

  20. Amma C, Krings T, Böer J et al. (2015) Advancing muscle-computer interfaces with high-density electromyography. In: Proceedings of the 33rd Annual ACM conference on human factors in computing systems. ACM, pp 929–938

  21. Ellis MD, Acosta AM, Yao J et al. (2007) Position-dependent torque coupling and associated muscle activation in the hemiparetic upper extremity. Exper Brain Res 176(4):594– 602

    Article  Google Scholar 

  22. Hargrove LJ, Englehart K, Hudgins B (2007) A comparison of surface and intramuscular myoelectric signal classification. IEEE Trans Biomed Eng 54(5):847–853

    Article  Google Scholar 

  23. Liu H, Hu B, Moore P (2015) HCI model with learning mechanism for cooperative design in pervasive computing environment. J Internet Technol 16(2):201–210

    Google Scholar 

  24. Matsubara T, Morimoto J (2013) Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface. IEEE Trans Biomed Eng 60(8):2205–2213

    Article  Google Scholar 

  25. Saponas TS, Tan DS, Morris D et al. (2009) Enabling always-available input with muscle-computer interfaces. In: Proceedings of the 22nd Annual ACM symposium on user interface software and technology. ACM,pp 167–176

  26. David RL, Cristian CL, Humberto LC (2015) Design of an electromyographic mouse. In: 2015 20th Symposium on signal processing, images and computer vision (STSIVA). IEEE, pp 1–8

  27. Khushaba RN (2014) Correlation analysis of electromyogram signals for multiuser myoelectric interfaces. IEEE Trans Neural Syst Rehabil Eng 22(4):745–755

    Article  Google Scholar 

  28. Lu Z, Chen X, Li Q et al. (2014) A hand gesture recognition framework and wearable gesture-based interaction prototype for mobile devices. IEEE Trans Human-Mach Syst 44(2):293–299

    Article  Google Scholar 

  29. Sapsanis C, Georgoulas G, Tzes A et al. (2013) Improving EMG based classification of basic hand movements using EMD. In: 2013 35th Annual International conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 5754–5757

  30. Huang NE, Shen Z, Long SR et al. (1971) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the royal society of London A: mathematical, physical and engineering sciences, vol 454. The Royal Society 1998, pp 903–995

  31. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  32. Breiman L, Friedman J, Stone CJ et al. (1984) Classification and regression trees[M]. CRC press

  33. Oskoei MA, Hu H (2007) Myoelectric control systems—a survey. Biomed Signal Process Control 2(4):275–294

    Article  Google Scholar 

  34. Phinyomark A, Limsakul C, Phukpattaranont P (2009) A novel feature extraction for robust EMG pattern recognition. arXiv:

  35. Rechy-Ramirez EJ, Hu H (2011) Stages for developing control systems using EMG and EEG signals: a survey. School of Computer Science and Electronic Engineering. University of Essex, pp 1744–8050

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This work is supported in part by the National Key Research and Development Plan of China (No. 2017YFB1002801), Natural Science Foundation of China (No.61502456, No.61572471), Beijing Science and Technology Committee, and Brain Science Research Program of Beijing (No.Z161100000216140).

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Correspondence to Yiqiang Chen.

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Zhang, Y., Chen, Y., Yu, H. et al. Wearing-independent hand gesture recognition method based on EMG armband. Pers Ubiquit Comput 22, 511–524 (2018).

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  • Human-computer interaction
  • Hand gesture recognition
  • Electromyographic (EMG)