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KNN Learning Techniques for Proportional Myocontrol in Prosthetics

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Converging Clinical and Engineering Research on Neurorehabilitation IV (ICNR 2020)

Part of the book series: Biosystems & Biorobotics ((BIOSYSROB,volume 28))

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Abstract

This work has been conducted in the context of pattern-recognition-based control for electromyographic prostheses. It presents a k-nearest neighbour (kNN) classification technique for gesture recognition, extended by a proportionality scheme. The methods proposed are practically implemented and validated. Datasets are captured by means of a state-of-the-art 8-channel electromyography (EMG) armband positioned on the forearm. Based on this data, the influence of kNN’s parameters is analyzed in pilot experiments. Moreover, the effect of proportionality scaling and rest thresholding schemes is investigated. A randomized, double-blind user study is conducted to compare the implemented method with the state-of-research algorithm Ridge Regression with Random Fourier Features (RR-RFF) for different levels of gesture exertion. The results from these experiments show a statistically significant improvement in favour of the kNN-based algorithm.

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References

  1. C. Cipriani, et al., Online myoelectric control of a dexterous hand prosthesis by transradial amputees. IEEE Trans. Neural Syst. Rehabil. Eng. 19(3), 260–270 (2011 )https://doi.org/10.1109/TNSRE.2011.2108667

  2. R.N. Khushaba, A. Al-Timemy, A. Al-Ani, A. Al-Jumaily, Myoelectric feature extraction using temporal-spatial descriptors for multifunction prosthetic hand control, in 38th Conference Proceedings IEEE Engineering Medical Biology Society (2016), pp. 1696–1699 https://doi.org/10.1109/EMBC.2016.7591042

  3. P. Geethanjali, Comparative study of pca in classification of multichannel EMG signals. APESM 38(2), 331–343 (2015) https://doi.org/10.1007/s13246-015-0343-8

  4. R.M.G. Tello, et al., Towards semg classification based on Bayesian and k-nn to control a prosthetic hand, in ISSNIP BRC (2013) https://doi.org/10.1109/BRC.2013.6487520

  5. Q.X. Li, et al., Improving robustness against electrode shift of semg based hand gesture recognition using online semi-supervised learning, in 2016 International Conference Machine Learn Cybernetics, vol. 1 (2016), pp. 344–349 https://doi.org/10.1109/ICMLC.2016.7860925

  6. H. Chen, et al., Exploring the relation between EMG sampling frequency and hand motion recognition accuracy, in IEEE International Conference System Man Cybernet (SMC) (2017), pp. 1139–1144 https://doi.org/10.1109/SMC.2017.8122765

  7. A. Ameri, E.N. Kamavuako, E.J. Scheme, K.B. Englehart, P.A. Parker, Support vector regression for improved real-time, simultaneous myoelectric control. IEEE Trans. Neural Syst. Rehabil. Eng. 22(6), 1198–1209 (2014)

    Article  Google Scholar 

  8. A. Gijsberts, et al., Stable myoelectric control of a hand prosthesis using non-linear incremental learning. Front. Neurorob. 8 (2014) https://doi.org/10.3389/fnbot.2014.00008

  9. S. Lobov, N. Krilova, I. Kastalskiy, V. Kazantsev, V. Makarov, A human-computer interface based on electromyography command-proportional control, in Proceedings of Neurotechnix, vol. 1 (2016), pp. 57–64 https://doi.org/10.5220/0006033300570064

  10. B. Hudgins, P.Parker, R.N. Scott, A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 40(1), 82–94 (1993) https://doi.org/10.1109/10.204774

  11. E. Scheme et al., Motion normalized proportional control for improved pattern recognition-based myoelectric control. IEEE Trans. Neural Syst. Rehabil. Eng. 22(1), 149–157 (2014)

    Article  Google Scholar 

  12. A.M. Simon, K. Stern, L.J. Hargrove, A comparison of proportional control methods for pattern recognition control, in Conference Proceedings IEEE Engineering Medical Biology Society (2011), pp. 3354–3357

    Google Scholar 

  13. S. Amsuess, P. Goebel, B. Graimann, D. Farina, A multi-class proportional myocontrol algorithm for upper limb prosthesis control: validation in real-life scenarios on amputees. IEEE Trans. Neural Syst. Rehabil. Eng. 23(5), 827–836 (2015)

    Article  Google Scholar 

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Correspondence to Tim Sziburis .

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Sziburis, T., Nowak, M., Brunelli, D. (2022). KNN Learning Techniques for Proportional Myocontrol in Prosthetics. In: Torricelli, D., Akay, M., Pons, J.L. (eds) Converging Clinical and Engineering Research on Neurorehabilitation IV. ICNR 2020. Biosystems & Biorobotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-030-70316-5_109

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  • DOI: https://doi.org/10.1007/978-3-030-70316-5_109

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70315-8

  • Online ISBN: 978-3-030-70316-5

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