Effects of Sampling Rate and Window Length on Motion Recognition Using sEMG Armband Module


The pattern-recognition-based control of myoelectric prostheses offers amputees a natural, intuitive approach to more finely control the prostheses. In this context, we recently developed a multichannel surface electromyography (sEMG) module with a low sampling rate and applied it for hand-motion research. In this study, we investigate the effects of the sEMG-signal sampling rate and feature extraction window length on the classification accuracy in hand-motion recognition. Ten normal subjects and one forearm amputee were made to wear an armband module consisting of eight EMG sensors, and seven and four hand movements of the normal subjects and amputee, respectively, were measured. The EMG signal was measured at 500 Hz and down-sampled to 250, 100, and 50 Hz. Four time-domain features (mean average value, waveform length, zero crossing, and slope sign change) were calculated as the sEMG features with six selected window lengths, which were increased in 50 ms intervals (50, 100, 150, 200, 250, and 300 ms). Hand-motion recognition was performed using artificial neural network, support vector machine, decision tree, and k-nearest neighbor classifiers. Our results showed that for all classifiers and all subjects, the hand-motion classification accuracy increases with an increase in the sampling rate and window length. We believe that our findings will aid in selecting the appropriate sampling rate and window length for prosthetics meant for daily use.

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  1. 1.

    Al-Timemy, A. H., Bugmann, G., Escudero, J., & Outram, N. (2013). Classification of finger movements for the dexterous hand prosthesis control with surface electromyography. IEEE Journal of Biomedical and Health Informatics, 17, 608–618. https://doi.org/10.1109/JBHI.2013.2249590

    Article  Google Scholar 

  2. 2.

    Ali, S., Samad, M., Mehmood, F., Ayaz, Y., Qazi, W. M., Khan, M. J., & Asgher, U. (2020). Hand gesture based control of NAO robot using myo armband. Springer International Publishing. https://doi.org/10.1007/978-3-030-20473-0_44

    Article  Google Scholar 

  3. 3.

    Chen, H., Zhang, Y., Zhang, Z., Fang, Y., Liu, H., & Yao, C. (2017). Exploring the relation between EMG sampling frequency and hand motion recognition accuracy. In 2017 IEEE international conference system man, cybernetics SMC 2017 2017-January (pp. 1139–1144) Doi: https://doi.org/10.1109/SMC.2017.8122765.

  4. 4.

    Clancy, E., Morin, E., & Merletti, R. (2002). Sampling, noise-reduction and amplitude estimation issues in surface electromyography. Journal of Electromyography and Kinesiology, 12, 1–16. https://doi.org/10.1016/S1050-6411(01)00033-5

    Article  Google Scholar 

  5. 5.

    Daley, H., Englehart, K., Hargrove, L., & Kuruganti, U. (2012). High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control. Journal of Electromyography and Kinesiology, 22, 478–484. https://doi.org/10.1016/j.jelekin.2011.12.012

    Article  Google Scholar 

  6. 6.

    De Luca, C. J. (2002). Surface electromyography: Detection and recording. DelSys Incorporated, 10.2, 1–10.

  7. 7.

    Englehart, K., & Hudgins, B. (2003). A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 50, 848–854. https://doi.org/10.1109/TBME.2003.813539

    Article  Google Scholar 

  8. 8.

    Farrell, T. R., & Weir, R. F. (2007). The optimal controller delay for myoelectric prostheses. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15, 111–118. https://doi.org/10.1109/TNSRE.2007.891391

    Article  Google Scholar 

  9. 9.

    Ghalyan IF, Abouelenin ZM, Kapila V (2019) Gaussian filtering of EMG signals for improved hand gesture classification. In 2018 IEEE signal processing in medicine and biology symposium SPMB 2018—proceedings (pp. 7–12). Doi: https://doi.org/10.1109/SPMB.2018.8615596.

  10. 10.

    Hargrove, L. J., Englehart, K., & Hudgins, B. (2007). A comparison of surface and intramuscular myoelectric signal classification. IEEE Transactions on Biomedical Engineering, 54, 847–853. https://doi.org/10.1109/TBME.2006.889192

    Article  Google Scholar 

  11. 11.

    Hudgins, B., Parker, P., & Scott, R. N. R. N. (1993). A new strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 40, 82–94. https://doi.org/10.1109/10.204774

    Article  Google Scholar 

  12. 12.

    Ives, J. C., & Wigglesworth, J. K. (2003). Sampling rate effects on surface EMG timing and amplitude measures. Clinical Biomechanics, 18, 543–552. https://doi.org/10.1016/S0268-0033(03)00089-5

    Article  Google Scholar 

  13. 13.

    Jayne, B. C., Lauder, G. V., Reilly, S. M., & Wainwright, P. C. (1990). Short communication. The effect of sampling rate on the analysis of digital electromyograms from vertebrate muscle. Journal of Experimental Biology, 154, 557–565.

    Article  Google Scholar 

  14. 14.

    Jørgensen, S. Å., & Fuglsang-Frederiksen, A. (1991). Turns-amplitude analysis at different sampling frequencies. Electroencephalogr Clin Neurophysiol Evoked Potentials, 81, 1–7. https://doi.org/10.1016/0168-5597(91)90097-H

    Article  Google Scholar 

  15. 15.

    Kim, K. S., Choi, H. H., Moon, C. S., & Mun, C. W. (2011). Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions. Current Applied Physics, 11, 740–745. https://doi.org/10.1016/j.cap.2010.11.051

    Article  Google Scholar 

  16. 16.

    Kim, S., Kim, J., Koo, B., Kim, T., Jung, H., Park, S., Kim, S., & Kim, Y. (2019). Development of an armband EMG module and a pattern recognition algorithm for the 5-finger myoelectric hand prosthesis. International Journal of Precision Engineering and Manufacturing, 20, 1997–2006. https://doi.org/10.1007/s12541-019-00195-w

    Article  Google Scholar 

  17. 17.

    Krishnan, K.S., Saha, A., Ramachandran, S., & Kumar, S. (2018). Recognition of human arm gestures using Myo armband for the game of hand cricket. In Proceedings—2017 IEEE 5th international symposium on robotics and intelligent sensors, IRIS 2017 2018-January (pp. 389–394). Doi: https://doi.org/10.1109/IRIS.2017.8250154.

  18. 18.

    Kurniawan, S. R., & Pamungkas, D. (2018). MYO Armband sensors and neural network algorithm for controlling hand robot. In Proceedings 2018 international conference on engineering ICAE 2018. Doi: https://doi.org/10.1109/INCAE.2018.8579153.

  19. 19.

    Li, G., Li, Y., Yu, L., & Geng, Y. (2011). Conditioning and sampling issues of EMG signals in motion recognition of multifunctional myoelectric prostheses. Annals of Biomedical Engineering, 39, 1779–1787. https://doi.org/10.1007/s10439-011-0265-x

    Article  Google Scholar 

  20. 20.

    Li, G., Li, Y., Zhang, Z., Geng, Y., & Zhou, R. (2010). Selection of sampling rate for EMG pattern recognition based prosthesis control. In 2010 Annual international conferences IEEE engineering in medicine and biology society EMBC’10 (pp. 5058–5061). Doi: https://doi.org/10.1109/IEMBS.2010.5626224

  21. 21.

    Nilsson, J., Panizza, M., & Hallett, M. (1993). Principles of digital sampling of a physiologic signal. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 89, 349–358. https://doi.org/10.1016/0168-5597(93)90075-Z

    Article  Google Scholar 

  22. 22.

    Phinyomark, A., Quaine, F., Charbonnier, S., Serviere, C., Tarpin-Bernard, F., & Laurillau, Y. (2013). EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Systems with Applications, 40, 4832–4840. https://doi.org/10.1016/j.eswa.2013.02.023

    Article  Google Scholar 

  23. 23.

    Politti, F., Casellato, C., Kalytczak, M. M., Garcia, M. B. S., & Biasotto-Gonzalez, D. A. (2016). Characteristics of EMG frequency bands in temporomandibullar disoders patients. Journal of Electromyography and Kinesiology, 31, 119–125. https://doi.org/10.1016/j.jelekin.2016.10.006

    Article  Google Scholar 

  24. 24.

    Powell, M. A., & Thakor, N. V. (2013). A training strategy for learning pattern recognition control for myoelectric prostheses. JPO Journal of Prosthetics and Orthotics, 25, 30–41. https://doi.org/10.1097/JPO.0b013e31827af7c1

    Article  Google Scholar 

  25. 25.

    Rawat, S., Vats, S., & Kumar, P. (2017). Evaluating and exploring the MYO ARMBAND. In Proceedings 5th international conference on system modeling and advancement in research trends, SMART 2016 (pp. 115–120). Doi: https://doi.org/10.1109/SYSMART.2016.7894501.

  26. 26.

    Samuel, O. W., Zhou, H., Li, X., Wang, H., Zhang, H., Sangaiah, A. K., & Li, G. (2018). Pattern recognition of electromyography signals based on novel time domain features for amputees’ limb motion classification. Computers & Electrical Engineering, 67, 646–655. https://doi.org/10.1016/j.compeleceng.2017.04.003

    Article  Google Scholar 

  27. 27.

    Sayin, F. S., Ozen, S., & Baspinar, U. (2018). Hand gesture recognition by using sEMG signals for human machine interaction applications. In Signal processing algorithms, architectures, arrangements, and applications conference proceedings, SPA 2018-September (pp. 27–30). Doi: https://doi.org/10.23919/SPA.2018.8563394.

  28. 28.

    Smith, L. H., Hargrove, L. J., Lock, B. A., & Kuiken, T. A. (2011). Determining the optimal window length for pattern recognition-based myoelectric control: Balancing the competing effects of classification error and controller delay. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19, 186–192. https://doi.org/10.1109/TNSRE.2010.2100828

    Article  Google Scholar 

  29. 29.

    Waris, A., Niazi, I. K., Jamil, M., Englehart, K., Jensen, W., & Kamavuako, E. N. (2019). Multiday evaluation of techniques for EMG-based classification of hand motions. IEEE Journal of Biomedical and Health Informatics, 23, 1526–1534. https://doi.org/10.1109/JBHI.2018.2864335

    Article  Google Scholar 

  30. 30.

    Zia-ur-Rehman, M., Waris, A., Gilani, S., Jochumsen, M., Niazi, I., Jamil, M., Farina, D., & Kamavuako, E. (2018). Multiday EMG-based classification of hand motions with deep learning techniques. Sensors, 18, 2497. https://doi.org/10.3390/s18082497

    Article  Google Scholar 

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This research was supported by the Bio & Medical Technology Development Program (2017M3A9E2063270) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.

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Correspondence to Youngho Kim.

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Kim, T., Kim, J., Koo, B. et al. Effects of Sampling Rate and Window Length on Motion Recognition Using sEMG Armband Module. Int. J. Precis. Eng. Manuf. 22, 1401–1411 (2021). https://doi.org/10.1007/s12541-021-00546-6

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  • Electromyography
  • Armband module
  • Sampling rate
  • Window length