Generalized Finger Motion Classification Model Based on Motor Unit Voting

in Motor Control
View More View Less
  • 1 East China University of Science and Technology
  • 2 Fudan University
Restricted access

Purchase article

USD  $24.95

Student 1 year online subscription

USD  $77.00

1 year online subscription

USD  $103.00

Student 2 year online subscription

USD  $147.00

2 year online subscription

USD  $195.00

Surface electromyogram-based finger motion classification has shown its potential for prosthetic control. However, most current finger motion classification models are subject-specific, requiring calibration when applied to new subjects. Generalized subject-nonspecific models are essential for real-world applications. In this study, the authors developed a subject-nonspecific model based on motor unit (MU) voting. A high-density surface electromyogram was first decomposed into individual MUs. The features extracted from each MU were then fed into a random forest classifier to obtain the finger label (primary prediction). The final prediction was selected by voting for all primary predictions provided by the decomposed MUs. Experiments conducted on 14 subjects demonstrated that our method significantly outperformed traditional methods in the context of subject-nonspecific finger motion classification models.

Liu and Zhou are with the School of Art Design and Media, East China University of Science and Technology, Shanghai, China. Dai and Chen are with the School of Information Science and Technology, Fudan University, Shanghai, China. Ye is with the School of Sports Science and Engineering, East China University of Science and Technology, Shanghai, China.

Zhou (myzhou@ecust.edu.cn) and Ye (13801751192@163.com) are corresponding authors.
  • Barsakcioglu, D.Y., & Farina, D. (2018). A real-time surface EMG decomposition system for non-invasive human–machine interfaces. Paper presented at the 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) (pp. 14). Cleveland, OH: IEEE. Retrieved from https://ieeexplore.ieee.org/document/8584659/

    • Search Google Scholar
    • Export Citation
  • Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123140.

  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 532. doi:10.1023/A:1010933404324

  • Chen, C., Yu, Y., Ma, S., Sheng, X., Lin, C., Farina, D., & Zhu, X. (2020). Hand gesture recognition based on motor unit spike trains decoded from high-density electromyography. Biomedical Signal Processing and Control, 55, 101637. doi:10.1016/j.bspc.2019.101637

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Côté-Allard, U., Fall, C.L., Drouin, A., Campeau-Lecours, A., Gosselin, C., Glette, K., Laviolette, F., … Gosselin, B. (2019). Deep learning for electromyographic hand hesture signal classification using transfer learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(4), 760771. PubMed ID: 30714928

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, C., Cao, Y., & Hu, X. (2019). Prediction of individual finger forces based on decoded motoneuron activities. Annals of Biomedical Engineering, 47(6), 13571368. PubMed ID: 30834478 doi:10.1007/s10439-019-02240-1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, C., & Hu, X. (2018). Extracting and classifying spatial muscle activation patterns in forearm flexor muscles using high-density electromyogram pecordings. International Journal of Neural Systems, 29(01), 1850025. doi:10.1142/S0129065718500259

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, C., & Hu, X. (2019a). Independent component analysis based algorithms for high-density electromyogram decomposition: Systematic evaluation through simulation. Computers in Biology and Medicine, 109, 171181. PubMed ID: 31059901 doi:10.1016/j.compbiomed.2019.04.033

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, C., & Hu, X. (2019b). Independent component analysis based algorithms for high-density electromyogram decomposition: Experimental evaluation of upper extremity muscles. Computers in Biology and Medicine, 108, 4248. PubMed ID: 31003178 doi:10.1016/j.compbiomed.2019.03.009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, C., & Hu, X. (2020). Finger joint angle estimation based on motoneuron discharge activities. IEEE Journal of Biomedical and Health Informatics, 24(3), 760767. PubMed ID: 31283514 doi:10.1109/JBHI.2019.2926307

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, C., Shin, H., Davis, B., & Hu, X. (2017). Origins of common neural inputs to different compartments of the extensor digitorum communis muscle. Scientific Reports, 7(1), 13960. PubMed ID: 29066852 doi:10.1038/s41598-017-14555-x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, C., Suresh, N.L., Suresh, A.K., Rymer, W.Z., & Hu, X. (2017). Altered motor unit discharge coherence in paretic muscles of stroke survivors. Frontiers in Neurology, 8, 202. PubMed ID: 28555126 doi:10.3389/fneur.2017.00202

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delisle-Rodriguez, D., Cardoso, V., Gurve, D., Loterio, F., Alejandra Romero-Laiseca, M., Krishnan, S., & Bastos-Filho, T. (2019). System based on subject-specific bands to recognize pedaling motor imagery: Towards a BCI for lower-limb rehabilitation. Journal of Neural Engineering, 16(5), 056005. PubMed ID: 30786265 doi:10.1088/1741-2552/ab08c8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Luca, C.J., & Merletti, R. (1988). Surface myoelectric signal cross-talk among muscles of the leg. Electroencephalography and Clinical Neurophysiology, 69(6), 568575. PubMed ID: 2453334 doi:10.1016/0013-4694(88)90169-1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Farina, D., Vujaklija, I., Sartori, M., Kapelner, T., Negro, F., Jiang, N., … Aszmann, O.C. (2017). Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation. Nature Biomedical Engineering, 1(2), 0025. doi:10.1038/s41551-016-0025

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gandevia, S.C., Burke, D., & McKeon, B. (1986). Coupling between human muscle spindle endings and motor units assessed using spike-triggered averaging. Neuroscience Letters, 71(2), 181186. PubMed ID: 2946995 doi:10.1016/0304-3940(86)90555-0

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holobar, A., & Zazula, D. (2007). Multichannel blind source separation using onvolution kernel compensation. IEEE Transactions on Signal Processing, 55(9), 44874496. doi:10.1109/TSP.2007.896108

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, X., Suresh, N.L., Xue, C., & Rymer, W.Z. (2015). Extracting extensor digitorum communis activation patterns using high-density surface electromyography. Frontiers in Physiology, 6, 279. https://www.frontiersin.org/articles/10.3389/fphys.2015.00279/full doi:10.3389/fphys.2015.00279

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hyvarinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3), 626634. PubMed ID: 18252563 doi:10.1109/72.761722

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, X., Ren, H., Xu, K., Ye, X., Dai, C., Clancy, E.A., … Chen, W. (2020). Quantifying spatial activation patterns of motor units in finger extensor muscles. IEEE Journal of Biomedical and Health Informatics, 11.

    • Search Google Scholar
    • Export Citation
  • Jiang, X., Xu, K., Zhang, R., Ren, H., & Chen, W. (2019). A redundancy removed, dual-tree, discrete wavelet transform to construct compact representations for automated seizure detection. Applied Sciences, 9(23), 5215. doi:10.3390/app9235215

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kato, R., Yokoi, H., & Arai, T. (2006). Real-time learning method for adaptable motion-discrimination using surface EMG signal. Paper presented at the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 21272132). Beijing, China: IEEE. http://ieeexplore.ieee.org/document/4058697/

    • Search Google Scholar
    • Export Citation
  • Keenan, K.G., Farina, D., Maluf, K.S., Merletti, R., & Enoka, R.M. (2005). Influence of amplitude cancellation on the simulated surface electromyogram. Journal of Applied Physiology, 98(1), 120131. PubMed ID: 15377649 doi:10.1152/japplphysiol.00894.2004

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, S.W., Wilson, K.M., Lock, B.A., & Kamper, D.G. (2011). Subject-specific myoelectric pattern classification of functional hand movements for stroke survivors. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(5), 558566. PubMed ID: 20876030 doi:10.1109/TNSRE.2010.2079334

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, Z., Chen, X., Li, Q., Zhang, X., & Zhou, P. (2014). A hand gesture recognition framework and wearable gesture-based interaction prototype for mobile devices. IEEE Transactions on Human–Machine Systems, 44(2), 293299. doi:10.1109/THMS.2014.2302794

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maimeri, M., Della Santina, C., Piazza, C., Rossi, M., Catalano, M.G., & Grioli, G. (2019). Design and assessment of control maps for multi-channel sEMG-driven prostheses and supernumerary limbs. Frontiers in Neurorobotics, 13, 26. https://www.frontiersin.org/articles/10.3389/fnbot.2019.00026/full doi:10.3389/fnbot.2019.00026

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martinez‐Valdes, E., Negro, F., Laine, C.M., Falla, D., Mayer, F., & Farina, D. (2017). Tracking motor units longitudinally across experimental sessions with high-density surface electromyography. The Journal of Physiology, 595(5), 14791496. PubMed ID: 28032343

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matsubara, T., & Morimoto, J. (2013). Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface. IEEE Transactions on Biomedical Engineering, 60(8), 22052213. PubMed ID: 23475334 doi:10.1109/TBME.2013.2250502

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Naik, G.R., Al-Timemy, A.H., & Nguyen, H.T. (2016). Transradial amputee gesture classification using an optimal number of sEMG sensors: An approach using ICA clustering. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(8), 837846. PubMed ID: 26394431 doi:10.1109/TNSRE.2015.2478138

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Naik, G.R., & Nguyen, H.T. (2015). Nonnegative matrix factorization for the identification of EMG finger movements: Evaluation using matrix analysis. IEEE Journal of Biomedical and Health Informatics, 19(2), 478485. PubMed ID: 25486650 doi:10.1109/JBHI.2014.2326660

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Negro, F., Muceli, S., Castronovo, A.M., Holobar, A., & Farina, D. (2016). Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation. Journal of Neural Engineering, 13(2), 026027026027. PubMed ID: 26924829 doi:10.1088/1741-2560/13/2/026027

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Orabona, F., Castellini, C., Caputo, B., Fiorilla, A.E., & Sandini, G. (2009). Model adaptation with least-squares SVM for adaptive hand prosthetics. Paper presented at the 2009 IEEE International Conference on Robotics and Automation (ICRA) (pp. 28972903). Kobe: IEEE. http://ieeexplore.ieee.org/document/5152247/

    • Search Google Scholar
    • Export Citation
  • Prahm, C., Paassen, B., Schulz, A., Hammer, B., & Aszmann, O. (2017). Transfer learning for rapid re-calibration of a myoelectric prosthesis after electrode shift. In J. Ibáñez, J. González-Vargas, J.M. Azorín, M. Akay, & J.L. Pons (Eds.), Converging clinical and engineering research on neurorehabilitation II, Biosystems & Biorobotics (Vol. 15, pp. 153157). Cham: Springer International Publishing. http://link.springer.com/10.1007/978-3-319-46669-9_28

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rahim, M.A., & Shin, J. (2020). Hand movement activity-based character input system on a virtual keyboard. Electronics, 9(5), 774. doi:10.3390/electronics9050774

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 590 590 138
Full Text Views 9 9 1
PDF Downloads 5 5 0