Abstract
Active engagement of human nervous system in the rehabilitation training is of great importance for the neurorehabilitation and motor function recovery of nerve injury patients. To this goal, the human motion intention should be detected and recognized in real time, which can be implemented by modeling the relationships between sEMG signals and the associated joint torques. However, present sEMG-torque modeling methods, including neuromusculoskeletal and black-box modeling methods, have their own deficiencies. Therefore, a hybrid modeling method based on the neuromuscular activations and Gaussian process regression (GPR) algorithm is proposed. Firstly, the preprocessed sEMG signals are converted into neural and muscular activations by the neuromusculoskeletal modeling method. The obtained muscle activations together with the associated joint angles are then transformed into the adjacent joint torques by a GPR algorithm to avoid the complicated modeling process of the muscle contraction dynamics, musculoskeletal geometry, and musculoskeletal dynamics. Moreover, the undetermined parameters of neuromuscular activation and GPR models are calibrated simultaneously based on an optimization algorithm designed in this study. Finally, the performance of the proposed method is demonstrated by validation and comparison experiments. It can be seen from the experiment results that, a high accuracy of torque prediction can be obtained using the proposed hybrid modeling method. Meanwhile, when the difference between the test and calibration trajectories is not very big, the joint torques for the test trajectory can be predicted with a high accuracy as well.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Corbyn, Z.: Stroke: a growing global burden. Nature, Outlook, 510(7506, pp. S2–S3, 06/26/print 2014
Lotze, M., Braun, C., Birbaumer, N., Anders, S., Cohen, L.G.: Motor learning elicited by voluntary drive. Brain 126, 866–872 (2003)
Young, A.J., Ferris, D.P.: State of the art and future directions for lower limb robotic exoskeletons. IEEE Trans. Neural Syst. Rehabil. Eng. 25(2), 171–182 (2017)
Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., Leonhardt, S.: A survey on robotic devices for upper limb rehabilitation. J. NeuroEng. Rehabil. 11(3), 29 (2014)
Krebs, H.I.: Rehabilitation robotics an academic engineer perspective. In: Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, pp. 6709–6713 (2011)
Zanotto, D., Stegall, P., Agrawal, S.K.: Adaptive assist-as-needed controller to improve gait symmetry in robot-assisted gait training. In: The Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, pp. 724–729 (2014)
Wang, W., Hou, Z., Cheng, L., et al.: Towards patients’ motion intention recognition: dynamics modeling and identification of iLeg - a lower limb rehabilitation robot under motion constraints. IEEE Trans. Syst. Man Cybern. Syst. 46(7), 980–992 (2016)
Foley, K.E.: Ideas in movement: the next wave of brain-computer interfaces. Nat. Med. 22(1), 1–5 (2016)
Soekadar, S.R., et al.: Hybrid EEG/EOG-based brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia. Science Robotics, 1(1) (2016)
Yang, D., Jiang, L., Huang, Q., Liu, R., Liu, H.: Experimental study of an EMG-controlled 5-DOF anthropomorphic prosthetic hand for motion restoration. J. Intell. Robot. Syst. 76(3), 427–441 (2014)
Tsukahara, A., Hasegawa, Y., Eguchi, K., Sankai, Y.: Restoration of gait for spinal cord injury patients using hal with intention estimator for preferable swing speed. IEEE Trans. Neural Syst. Rehabil. Eng. 23(2), 308–318 (2015)
Duan, F., Dai, L., Chang, W., Chen, Z., Zhu, C., Li, W.: sEMG-based identification of hand motion commands using wavelet neural network combined with discrete wavelet transform. IEEE Trans. Ind. Electron. 63(3), 1923–1934 (2016)
Jarrassé, N., et al.: Classification of phantom finger, hand, wrist, and elbow voluntary gestures in transhumeral amputees with sEMG. IEEE Trans. Neural Syst. Rehabil. Eng. 25(1), 71–80 (2017)
Zhang, F., et al.: sEMG-based continuous estimation of joint angles of human legs by using BP neural network. Neurocomputing 78(1), 139–148 (2012)
Han, J., Ding, Q., Xiong, A., Zhao, X.: A state-space EMG model for the estimation of continuous joint movements. IEEE Trans. Ind. Electron. 62(7), 4267–4275 (2015)
Buchanan, T.S., Lloyd, D.G., Manal, K., Besier, T.F.: Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command. J. Appl. Biomech. 20(4), 367–395 (2004)
Fleischer, C., Hommel, G.: A human-exoskeleton interface utilizing electromyography. IEEE Trans. Robot. 24(4), 872–882 (2008)
Ao, D., Song, R., Gao, J.: Movement performance of human-robot cooperation control based on EMG-driven hill-type and proportional models for an ankle power-assist exoskeleton robot. IEEE Trans. Neural Syst. Rehabil. Eng. 25(8), 1125–1134 (2017)
Meng, W., Ding, B., Zhou, Z., Liu, Q., Ai, Q.: An EMG-based force prediction and control approach for robot-assisted lower limb rehabilitation. In: Proceedings of the 2014 IEEE International Conference on Systems, Man and Cybernetics, pp. 2198–2203 (2014)
Corcos, D.M., Gottlieb, G.L., Latash, M.L., Almeida, G.L., Agarwal, G.C.: Electromechanical delay: an experimental artifact. J. Electromyogr. Kinesiol. 2(2), 59–68 (1992)
Lloyd, D.G., Besier, T.F.: An EMG-driven musculoskeletal model for estimation of the human knee joint moments across varied tasks. J. Biomech. 36, 765–776 (2003)
Acknowledgments
This research is supported by National Key R&D Program of China (Grant 2017YFB1302303), National Natural Science Foundation of China (Grant 91848110), and Beijing Natural Science Foundation (Grants 3171001 and L172050).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, W. et al. (2019). Neuromuscular Activation Based SEMG-Torque Hybrid Modeling and Optimization for Robot Assisted Neurorehabilitation. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11954. Springer, Cham. https://doi.org/10.1007/978-3-030-36711-4_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-36711-4_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36710-7
Online ISBN: 978-3-030-36711-4
eBook Packages: Computer ScienceComputer Science (R0)