Skip to main content
SpringerLink
Log in

Channel selection against electrode shift enables robust myoelectric control without retraining

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

Myoelectric controlled interfaces driven by muscle activities have achieved good performance in ideal conditions and showed many potential medical-related and industrial applications. However, in practical applications, the performance could be drastically degraded due to the electrode (sensor) shift, which is inevitable in donning and doffing the system. In this study, we presented a novel channel selection method against electrode shift for robust pattern-recognition based myoelectric control. The proposed method was evaluated on twenty-four subjects, including twenty-two able-bodied subjects and two amputees, and compared with two traditional channel selection methods, i.e., uniform selection (UNI) and sequential feature selection (SFS). We demonstrated that the offline error rates of the proposed method were significantly lower than those of the other two methods (P<0.05), and its online performance in shift conditions was comparable to that in ideal conditions. These outcomes benefit the practical applications of robust myoelectric controlled interfaces.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Rodriguez-Tapia B, Soto I, Martinez D M, et al. Myoelectric interfaces and related applications: Current state of EMG signal processing—A systematic review. IEEE Access, 2020, 8: 7792–7805

    Article  Google Scholar 

  2. Hargrove L J, Miller L A, Turner K, et al. Myoelectric pattern recognition outperforms direct control for transhumeral amputees with targeted muscle reinnervation: A randomized clinical trial. Sci Rep, 2017, 7: 247–255

    Article  Google Scholar 

  3. Zhuang Y, Leng Y, Zhou J, et al. Voluntary control of an ankle joint exoskeleton by able-bodied individuals and stroke survivors using EMG-based admittance control scheme. IEEE Trans Biomed Eng, 2021, 68: 695–705

    Article  Google Scholar 

  4. Hakonen M, Piitulainen H, Visala A. Current state of digital signal processing in myoelectric interfaces and related applications. Biomed Signal Process Control, 2015, 18: 334–359

    Article  Google Scholar 

  5. Tortora S, Moro M, Menegatti E. Dual-myo real-time control of a humanoid arm for teleoperation. In: International Conference on Human-Robot Interaction. Daegu, 2020. 245–249

  6. DelPreto J, Rus D. Sharing the load: Human-robot team lifting using muscle activity. In: International Conference on Robotics and Automation. Montreal, 2019. 7906–7912

  7. Simão M, Mendes N, Gibaru O, et al. A review on electromyography decoding and pattern recognition for human-machine interaction. IEEE Access, 2019, 7: 39564–39582

    Article  Google Scholar 

  8. Dellacasa Bellingegni A, Gruppioni E, Colazzo G, et al. NLR, MLP, SVM, and LDA: A comparative analysis on EMG data from people with trans-radial amputation. J NeuroEng Rehabil, 2017, 14: 82

    Article  Google Scholar 

  9. He J, Sheng X, Zhu X, et al. Electrode density affects the robustness of myoelectric pattern recognition system with and without electrode shift. IEEE J Biomed Health Inform, 2019, 23: 156–163

    Article  Google Scholar 

  10. Tolooshams B, Jiang N. Robustness of frequency division technique for online myoelectric pattern recognition against contraction-level variation. Front Bioeng Biotechnol, 2017, 5: 3

    Article  Google Scholar 

  11. Tkach D, Huang H, Kuiken T A. Study of stability of time-domain features for electromyographic pattern recognition. J Neuroeng Rehabil, 2010, 7: 21

    Article  Google Scholar 

  12. Young A J, Hargrove L J, Kuiken T A. The effects of electrode size and orientation on the sensitivity of myoelectric pattern recognition systems to electrode shift. IEEE Trans Biomed Eng, 2011, 58: 2537–2544

    Article  Google Scholar 

  13. He J, Joshi M V, Chang J, et al. Efficient correction of armband rotation for myoelectric-based gesture control interface. J Neural Eng, 2020, 17: 036025

    Article  Google Scholar 

  14. Young A J, Hargrove L J, Kuiken T A. Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration. IEEE Trans Biomed Eng, 2012, 59: 645–652

    Article  Google Scholar 

  15. Zhang X, Wu L, Yu B, et al. Adaptive calibration of electrode array shifts enables robust myoelectric control. IEEE Trans Biomed Eng, 2020, 67: 1947–1957

    Google Scholar 

  16. Stango A, Negro F, Farina D. Spatial correlation of high-density EMG signals provides features robust to electrode number and shift in pattern recognition for myocontrol. IEEE Trans Neural Syst Rehabil Eng, 2014, 23: 189–198

    Article  Google Scholar 

  17. Hargrove L, Englehart K, Hudgins B. A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control. Biomed Signal Process Control, 2008, 3: 175–180

    Article  Google Scholar 

  18. Huang G, Xian Z, Tang F, et al. Low-density surface electromyographic patterns under electrode shift: Characterization and NMF-based classification. Biomed Signal Process Control, 2020, 59: 101890

    Article  Google Scholar 

  19. He J, Sheng X, Zhu X, et al. Spatial information enhances myoelectric control performance with only two channels. IEEE Trans Ind Inf, 2019, 15: 1226–1233

    Article  Google Scholar 

  20. Huang H, Zhou P, Li G, et al. An analysis of emg electrode configuration for targeted muscle reinnervation based neural machine interface. IEEE Trans Neural Syst Rehabil Eng, 2008, 16: 37–45

    Article  Google Scholar 

  21. Daley H, Englehart K, Hargrove L, et al. High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control. J Electromyogr Kinesiol, 2012, 22: 478–484

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Hwang H J, Mathias Hahne J, Müller K R. Channel selection for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom. J Neural Eng, 2014, 11: 056008

    Article  Google Scholar 

  24. He J, Zhu X. Combining improved gray-level co-occurrence matrix with high density grid for myoelectric control robustness to electrode shift. IEEE Trans Neural Syst Rehabil Eng, 2017, 25: 1539–1548

    Article  Google Scholar 

  25. Menon R, Di Caterina G, Lakany H, et al. Study on interaction between temporal and spatial information in classification of EMG signals for myoelectric prostheses. IEEE Trans Neural Syst Rehabil Eng, 2017, 25: 1832–1842

    Article  Google Scholar 

  26. Waris A, Mendez I, Englehart K, et al. On the robustness of real-time myoelectric control investigations: A multiday Fitts’ law approach. J Neural Eng, 2019, 16: 026003

    Article  Google Scholar 

  27. Hudgins B, Parker P, Scott R N. A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng, 1993, 40: 82–94

    Article  Google Scholar 

  28. Lv B, Sheng X, Hao D, et al. Relationship between offline and online metrics in myoelectric pattern recognition control based on target achievement control test. In: International Conference of the IEEE Engineering in Medicine and Biology Society. Berlin, 2019. 6595–6598

  29. Li G, Schultz A E, Kuiken T A. Quantifying pattern recognition—Based myoelectric control of multifunctional transradial prostheses. IEEE Trans Neural Syst Rehabil Eng, 2010, 18: 185–192

    Article  Google Scholar 

  30. Farrell T R, Weir R F F. A comparison of the effects of electrode implantation and targeting on pattern classification accuracy for prosthesis control. IEEE Trans Biomed Eng, 2008, 55: 2198–2211

    Article  Google Scholar 

  31. Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. J Rehabil Res Dev, 2011, 48: 643–659

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Bo Lv, XinJun Sheng, Han Ding & XiangYang Zhu

  2. Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada

    JiaYuan He

Authors
  1. Bo Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. JiaYuan He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. XinJun Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Han Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. XiangYang Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to XinJun Sheng.

Additional information

This work was supported by the China National Key R&D Program (Grant No. 2018YFB1307200), the National Natural Science Foundation of China (Grant Nos. 91948302, 51620105002), and the Science and Technology Commission of Shanghai Municipality (Grant No. 18JC1410400).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lv, B., He, J., Sheng, X. et al. Channel selection against electrode shift enables robust myoelectric control without retraining. Sci. China Technol. Sci. 64, 1653–1662 (2021). https://doi.org/10.1007/s11431-021-1842-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-021-1842-3

Keywords

Search

Navigation