Skip to main content
SpringerLink
Log in

Enhanced myoelectric control against arm position change with weighted recursive Gaussian process

  • Special Issue on Computational Intelligence-based Control and Estimation in Mechatronic Systems
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In the last decade, there has been huge advancement in biomechatronic systems by the integration of pattern recognition and regression algorithms. In many myoelectric control studies, high accuracy in estimating a subject’s wrist movement was reported by measuring electromyography (EMG) signal from subjects’ forearms. However, many algorithms suffer from limited robustness against undesired disturbance in the real-world environment. In particular, arm position change is an inevitable disturbance that results in severe degradation of performance. In this study, the weighted recursive Gaussian process (WRGP) is proposed to overcome this effect. In the algorithm, the noise variance is weighted by covariate shift adaptation, which is able to handle the uncertainties. WRGP is compared with the commonly used linear regression (LR) and multilayer perceptron (MLP). LR, MLP, and WRGP are trained with the EMG dataset acquired at an arm position and tested with the different EMG dataset acquired at another arm position. Also, WRGP with uniform weights and WRGP with the weights estimated from the covariate shift adaptation are compared. The results show that WRGP with uniform weights is more robust than LR and MLP regardless of the arm position (0.16 and 0.16 higher average \(R^2\) index, respectively). The performance of WRGP with the weights estimated from the covariate shift adaptation is significantly higher (\(p<0.05\)) than the performance of LR and MLP (0.30 and 0.33 higher average \(R^2\) index, respectively).

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Qi J, Jiang G, Li G, Sun Y, Tao B (2020) Surface EMG hand gesture recognition system based on PCA and GRNN. Neural Comput Appl 32(10):6343–6351. https://doi.org/10.1007/s00521-019-04142-8

    Article  Google Scholar 

  2. Yang D, Gu Y, Thakor NV, Liu H (2019) Improving the functionality, robustness, and adaptability of myoelectric control for dexterous motion restoration. Exp Brain Res 237(2):291–311. https://doi.org/10.1007/s00221-018-5441-x

    Article  Google Scholar 

  3. Farina D, Jiang N, Rehbaum H, Holobar A, Graimann B, Dietl H, Aszmann OC (2014) The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges. IEEE Trans Neural Syst Rehabil Eng 22(4):797–809. https://doi.org/10.1109/TNSRE.2014.2305111

    Article  Google Scholar 

  4. Scheme E, Fougner A, Stavdahl Ø, Chan ADC, Englehart K (2010) Examining the adverse effects of limb position on pattern recognition based myoelectric control. In: 2010 annual international conference of the IEEE engineering in medicine and biology, pp 6337–6340

  5. Geng Y, Zhou P, Li G (2012) Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees. J NeuroEng Rehabil 9(1):74. https://doi.org/10.1186/1743-0003-9-74

    Article  Google Scholar 

  6. Liu J, Zhang D, Sheng X, Zhu X (2014) Quantification and solutions of arm movements effect on sEMG pattern recognition. Biomed Signal Process Control 13:189–197. https://doi.org/10.1016/j.bspc.2014.05.001

    Article  Google Scholar 

  7. Jiang N, Muceli S, Graimann B, Farina D (2013) Effect of arm position on the prediction of kinematics from EMG in amputees. Med Biol Eng Comput 51(1):143–151. https://doi.org/10.1007/s11517-012-0979-4

    Article  Google Scholar 

  8. Hwang HJ, Hahne JM, Müller KR (2017) Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing. PLoS ONE 12(11):e0186318. https://doi.org/10.1371/journal.pone.0186318

    Article  Google Scholar 

  9. Hahne JM, Schweisfurth MA, Koppe M, Farina D (2018) Simultaneous control of multiple functions of bionic hand prostheses: performance and robustness in end users. Sci Robot 3(19):eaat3630. https://doi.org/10.1126/scirobotics.aat3630

    Article  Google Scholar 

  10. Fougner A, Scheme E, Chan ADC, Englehart K, Stavdahl Ø (2011) Resolving the Limb position effect in myoelectric pattern recognition. IEEE Trans Neural Syst Rehabil Eng 19(6):644–651. https://doi.org/10.1109/TNSRE.2011.2163529

    Article  Google Scholar 

  11. Yang D, Yang W, Huang Q, Liu H (2017) Classification of multiple finger motions during dynamic upper limb movements. IEEE J Biomed Health Inform 21(1):134–141. https://doi.org/10.1109/JBHI.2015.2490718

    Article  Google Scholar 

  12. Park K, Suk H, Lee S (2016) Position-independent decoding of movement intention for proportional myoelectric interfaces. IEEE Trans Neural Syst Rehabil Eng 24(9):928–939

    Article  Google Scholar 

  13. Vidovic MM, Paredes LP, Hwang H, Amsuss S, Pahl J, Hahne JM, Graimann B, Farina D, Müller K (2014) Covariate shift adaptation in EMG pattern recognition for prosthetic device control. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society, pp 4370–4373

  14. Vidovic MM, Hwang H, Amsüss S, Hahne JM, Farina D, Müller K (2016) Improving the robustness of myoelectric pattern recognition for upper limb prostheses by covariate shift adaptation. IEEE Trans Neural Syst Rehabil Eng 24(9):961–970

    Article  Google Scholar 

  15. Kanoga S, Kanemura A (2018) Assessing the effect of transfer learning on myoelectric control systems with three electrode positions. In: 2018 IEEE International Conference on Industrial Technology (ICIT), pp 1478–1483

  16. Gretton A, Smola A, Huang J, Schmittfull M, Borgwardt K, Schölkopf B (2013) Covariate shift by kernel mean matching. In: Dataset shift in machine learning. https://doi.org/10.7551/mitpress/9780262170055.003.0008

  17. Hahne JM, Dähne S, Hwang H, Müller K, Parra LC (2015) Concurrent adaptation of human and machine improves simultaneous and proportional myoelectric control. IEEE Trans Neural Syst Rehabil Eng 23(4):618–627. https://doi.org/10.1109/TNSRE.2015.2401134

    Article  Google Scholar 

  18. Hahne JM, Markovic M, Farina D (2017) User adaptation in myoelectric man-machine interfaces. Sci Rep 7(1):4437. https://doi.org/10.1038/s41598-017-04255-x

    Article  Google Scholar 

  19. Couraud M, Cattaert D, Paclet F, Oudeyer PY, de Rugy A (2018) Model and experiments to optimize co-adaptation in a simplified myoelectric control system. J Neural Eng 15(2):26006. https://doi.org/10.1088/1741-2552/aa87cf

    Article  Google Scholar 

  20. Kapelner T, Vujaklija I, Jiang N, Negro F, Aszmann OC, Principe J, Farina D (2019) Predicting wrist kinematics from motor unit discharge timings for the control of active prostheses. J Neuroeng Rehabil 16(1):47. https://doi.org/10.1186/s12984-019-0516-x

    Article  Google Scholar 

  21. Nielsen JLG, Holmgaard S, Jiang N, Englehart KB, Farina D, Parker PA (2011) Simultaneous and proportional force estimation for multifunction myoelectric prostheses using mirrored bilateral training. IEEE Trans Biomed Eng 58(3):681–688. https://doi.org/10.1109/TBME.2010.2068298

    Article  Google Scholar 

  22. Muceli S, Farina D (2012) Simultaneous and proportional estimation of hand kinematics from EMG during mirrored movements at multiple degrees-of-freedom. IEEE Trans Neural Syst Rehabil Eng 20(3):371–378. https://doi.org/10.1109/TNSRE.2011.2178039

    Article  Google Scholar 

  23. Jiang N, Vest-Nielsen JL, Muceli S, Farina D (2012) EMG-based simultaneous and proportional estimation of wrist/hand kinematics in uni-lateral trans-radial amputees. J Neuroeng Rehabil 9:42. https://doi.org/10.1186/1743-0003-9-42

    Article  Google Scholar 

  24. Ameri A, Scheme EJ, Kamavuako EN, Englehart KB, Parker PA (2014) Real-time, simultaneous myoelectric control using force and position-based training paradigms. IEEE Trans Biomed Eng 61(2):279–287. https://doi.org/10.1109/TBME.2013.2281595

    Article  Google Scholar 

  25. Rasmussen CE, Williams CKI (2018) Gaussian processes for machine learning. MIT Press, Cambridge. https://doi.org/10.7551/mitpress/3206.001.0001

  26. Sheng H, Xiao J, Cheng Y, Ni Q, Wang S (2018) Short-term solar power forecasting based on weighted Gaussian process regression. IEEE Trans Ind Electron. https://doi.org/10.1109/TIE.2017.2714127

  27. Feurer M, Letham B, Eytan Bakshy F (2018) Scalable meta-learning for bayesian optimization using ranking-weighted Gaussian process ensembles. In: AutoML workshop ICML 2018

  28. Csató L, Opper M (2002) Sparse on-line Gaussian processes. Neural Comput. https://doi.org/10.1162/089976602317250933

  29. Van Vaerenbergh S, Lazaro-Gredilla M, Santamaria I (2012) Kernel recursive least-squares tracker for time-varying regression. IEEE Trans Neural Netw Learn Syst 23(8):1313–1326. https://doi.org/10.1109/TNNLS.2012.2200500

    Article  Google Scholar 

  30. Ameri A, Kamavuako EN, Scheme EJ, Englehart KB, Parker PA (2014) Support vector regression for improved real-time, simultaneous myoelectric control. IEEE Trans Neural Syst Rehabil Eng 22(6):1198–1209. https://doi.org/10.1109/TNSRE.2014.2323576

    Article  Google Scholar 

  31. Hahne JM, Biessmann F, Jiang N, Rehbaum H, Farina D, Meinecke FC, Muller KR, Parra LC (2014) Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control. IEEE Trans Neural Syst Rehabil Eng 22(2):269–279. https://doi.org/10.1109/TNSRE.2014.2305520

    Article  Google Scholar 

  32. Gijsberts A, Bohra R, Sierra González D, Werner A, Nowak M, Caputo B, Roa MA, Castellini C (2014) Stable myoelectric control of a hand prosthesis using non-linear incremental learning. Front Neurorobot. https://doi.org/10.3389/fnbot.2014.00008

  33. Krasoulis A, Vijayakumar S, Nazarpour K (2015) Evaluation of regression methods for the continuous decoding of finger movement from surface EMG and accelerometry. In: 2015 7th international IEEE/EMBS conference on neural engineering (NER), pp 631–634. https://doi.org/10.1109/NER.2015.7146702

  34. Sayed AH (2003) Fundamentals of adaptive filtering. Wiley, New York

    Google Scholar 

  35. D’Avella A, Portone A, Fernandez L, Lacquaniti F (2006) Control of fast-reaching movements by muscle synergy combinations. J Neurosci. https://doi.org/10.1523/JNEUROSCI.0830-06.2006

  36. Perez-Cruz F, Van Vaerenbergh S, Murillo-Fuentes JJ, Lazaro-Gredilla M, Santamaria I (2013) Gaussian processes for nonlinear signal processing: an overview of recent advances. https://doi.org/10.1109/MSP.2013.2250352

  37. RojoÁlvarez JL, Martínez Ramón M, MuñozMarí J, CampsValls G (2018) Digital signal processing with kernel methods. Wiley, Chichester. https://doi.org/10.1002/9781118705810

  38. Vegetabile BG, Gillen DL, Stern HS (2020) Optimally balanced Gaussian process propensity scores for estimating treatment effects. J R Stat Soc Ser A Stat Soc. https://doi.org/10.1111/rssa.12502

  39. Kügelgen J, Mey A, Loog M (2020) Semi-generative modelling: covariate-shift adaptation with cause and effect features. In: AISTATS 2019—22nd international conference on artificial intelligence and statistics. arXiv:1807.07879

  40. Jiang N, Rehbaum H, Vujaklija I, Graimann B, Farina D (2014) Intuitive, online, simultaneous, and proportional myoelectric control over two degrees-of-freedom in upper limb amputees. IEEE Trans Neural Syst Rehabil Eng. https://doi.org/10.1109/TNSRE.2013.2278411

  41. Smith LH, Kuiken TA, Hargrove LJ (2016) Evaluation of linear regression simultaneous myoelectric control using intramuscular EMG. IEEE Trans Biomed Eng 63(4):737–746. https://doi.org/10.1109/TBME.2015.2469741

    Article  Google Scholar 

  42. Li Y, Kambara H, Koike Y, Sugiyama M (2010) Application of covariate shift adaptation techniques in brain-computer interfaces. IEEE Trans Biomed Eng. https://doi.org/10.1109/TBME.2009.2039997

  43. Spüler M, Rosenstiel W, Bogdan M (2012) Principal component based covariate shift adaption to reduce non-stationarity in a MEG-based brain-computer interface. Eurasip J Adv Signal Process. https://doi.org/10.1186/1687-6180-2012-129

  44. Chowdhury A, Raza H, Meena YK, Dutta A, Prasad G (2018) Online covariate shift detection-based adaptive brain-computer interface to trigger hand exoskeleton feedback for neuro-rehabilitation. IEEE Trans Cognit Dev Syst. https://doi.org/10.1109/TCDS.2017.2787040

  45. Raza H, Rathee D, Zhou SM, Cecotti H, Prasad G (2019) Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.04.087

  46. Yamada M, Sigal L, Raptis M (2014) Covariate shift adaptation for discriminative 3D pose estimation. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2013.123

  47. Wichern G, Yamada M, Thornburg H, Sugiyama M, Spanias A (2010) Automatic audio tagging using covariate shift adaptation. In: ICASSP, IEEE international conference on acoustics, speech and signal processing—proceedings. https://doi.org/10.1109/ICASSP.2010.5495973

Download references

Author information

Authors and Affiliations

  1. Department of Telecommunications, Electrical, Robotics, and Biomedical Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia

    Myong Chol Jung, Rifai Chai & Jinchuan Zheng

  2. Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia

    Hung Nguyen

Authors
  1. Myong Chol Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rifai Chai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinchuan Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hung Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Myong Chol Jung.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jung, M.C., Chai, R., Zheng, J. et al. Enhanced myoelectric control against arm position change with weighted recursive Gaussian process. Neural Comput & Applic 34, 5015–5028 (2022). https://doi.org/10.1007/s00521-021-05743-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-05743-y

Keywords

Search

Navigation