Abstract
The aim of the study was to apply the real-time surface electromyography (EMG) decomposition to the continuous estimation of grasp kinematics. A real-time decomposition scheme based on the convolutional compensation kernel algorithm was proposed. High-density surface EMG signals and grasp kinematics were recorded concurrently from five able-bodied subject. The electro-mechanical delay between identified motor unit activities and grasp kinematics was characterized and utilized to optimize the multiple linear regression model for the grasp estimation. The discharge rate of each motor unit was extracted as the feature input to the regression model. On average, \(36\pm 15\) motor units were identified during each grasp task. The average root mean square error between estimated grasp kinematics and actual recorded signals was \(0.21\pm 0.05\), with the average delay of \(212\pm 50\) ms for the feature. The computation efficiency of the decomposition scheme and the high estimation accuracy imply the practical application for human-machine interfaces based on neural signals.
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References
Paciga, J.E., Richard, P.D., Scott, R.N.: Error rate in five-state myoelectric control systems. Med. Biol. Eng. Compu. 18(3), 287–290 (1980)
Salisbury, J.K., Craig, J.J.: Articulated hands: force control and kinematic issues. Int J. Robot. Res. 1(1), 4–17 (1982)
Resnik, L., Huang, H., Winslow, A., Crouch, D.L., Zhang, F., Wolk, N.: Evaluation of EMG pattern recognition for upper limb prosthesis control: a case study in comparison with direct myoelectric control. J. Neuroeng. Rehabil. 15(1), 23 (2018)
Jiang, N., Englehart, K.B., Parker, P.A.: Extracting simultaneous and proportional neural control information for multiple-DOF prostheses from the surface electromyographic signal. IEEE Trans. Biomed. Eng. 56(4), 1070–1080 (2009)
Lin, C., Wang, B., Jiang, N., Farina, D.: Robust extraction of basis functions for simultaneous and proportional myoelectric control via sparse non-negative matrix factorization. J. Neural Eng. 15(2), 026017 (2018)
Biddiss, E., Chau, T.: Upper-limb prosthetics: critical factors in device abandonment. Am. J. Phys. Med. Rehabil. 86(12), 977–987 (2007)
Holobar, A., Farina, D.: Blind source identification from the multichannel surface electromyogram. Physiol. Meas. 35(7), R143 (2014)
Negro, F., Muceli, S., Castronovo, A.M., Holobar, A., Farina, D.: Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation. J. Neural Eng. 13(2), 026027 (2016)
Farina, D., et al.: 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 (2014)
Weinberger, M., Dostrovsky, J.O.: Motor unit. Encyclopedia of Movement Disorders, pp. 204–206 (2010)
Farina, D., et al.: Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation. Nat. Biomed. Eng. 1(2), 0025 (2017)
Kapelner, T., Negro, F., Aszmann, O.C., Farina, D.: Decoding motor unit activity from forearm muscles: perspectives for myoelectric control. IEEE Trans. Neural Syst. Rehabil. Eng. 26(1), 244–251 (2018)
Wardowski, M.D., Roy, S.H., Li, Z., Contessa, P., De Luca, G., Kline, J.C.: Motor unit drive: a neural interface for real-time upper limb prosthetic control. J. Neural Eng. 16(1), 016012 (2019)
Chen, C., Guohong, C., WeiChao, G., Xinjun, S., Dario, F., Xiangyang, Z.: Prediction of finger kinematics from discharge timings of motor units: implications for intuitive control of myoelectric prostheses. J. Neural Eng. 16(2), 026005 (2019)
Holobar, A., Zazula, D.: Multichannel blind source separation using convolution kernel compensation. IEEE Trans. Signal Process. 55(9), 4487–4496 (2007)
Holobar, A., Zazula, D.: Gradient convolution kernel compensation applied to surface electromyograms. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds.) ICA 2007. LNCS, vol. 4666, pp. 617–624. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74494-8_77
Holobar, A., Minetto, M.A., Farina, D.: Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric. J. Neural Eng. 11(1), 016008 (2014)
Savc, M., Glaser, V., Kranjec, J., Cikajlo, I., Matjacic, Z., Holobar, A.: Comparison of convolutive kernel compensation and non-negative matrix factorization of surface electromyograms. IEEE Trans. Neural Syst. Rehabil. Eng. 26(10), 1935–1944 (2018)
Liu, L., Bonato, P., Clancy, E.A.: Comparison of methods for estimating motor unit firing rate time series from firing times. J. Electromyogr. Kinesiol. 31, 22–31 (2016)
Ngeo, J.G., Tamei, T., Shibata, T.: Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model. J. Neuroeng. Rehabil. 11(1), 122 (2014)
M Hioki and H Kawasaki. Estimation of finger joint angles from sEMG using a neural network including time delay factor and recurrent structure. ISRN Rehabil. 2012 (2012)
van Dieen, J.H., Thissen, C.E.A.M., van de Ven, A.J.G.M., Toussaint, H.M.: The electro-mechanical delay of the erector spinae muscle: influence of rate of force development, fatigue and electrode location. Eur. J. Appl. Physiol. 63(3), 216–222 (1991)
Acknowledgments
The authors would like to thank all the subjects for their participation in the study. This work was funded by in part the National Natural Science Foundation of China (No. 91748119, No. 51620105002), and by the Science and Technology Commission of Shanghai Municipality (No. 18JC1410400).
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Chen, C., Ma, S., Sheng, X., Zhu, X. (2019). Continuous Estimation of Grasp Kinematics with Real-Time Surface EMG Decomposition. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11744. Springer, Cham. https://doi.org/10.1007/978-3-030-27541-9_10
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