Abstract
Several linear and nonlinear models were proposed to predict the forward relationship between EMG signals and kinematics for biomechanical limbs, which is meaningful for EMG-based control. Although using nonlinear model to predict the kinematics is able to represent rational complex relationship between EMG signals and desired outputs, there exists high risk for overfitting models to training data and calculating burden because of the multi-channel variation EMG signals. Inspired by the hypothesis that CNS modulates muscle synergies to simplify the motor control and learning of coordinating variation of redundant joints, this paper proposed to extract the synergies to reduce the dimension of EMG-based control. Furthermore, the fuzzy wavelet neural network was developed to generate velocity–adapted gait by the reference gaits only with the limited set of experimental trials. The experimental results show the efficiency and robust of this approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jing, N., Vujaklija, I., Rehbaum, H., Graimann, B., Farina, D.: Is accurate mapping of EMG signals on kinematics needed for precise online myoelectric control? IEEE Trans. Neural Syst. Rehabil. Eng. 22(5), 49–558 (2014)
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, 1070–1080 (2009)
Muceli, S., Jiang, N., Farina, D.: Extracting signals robust to electrode number and shift for online simultaneous and proportional myoelectric control by factorization algorithms. IEEE Trans. Neural Syst. Rehabil. Eng. 22, 623–633 (2014)
Hahne, J.M., Biessmann, F., Jiang, N., Rehbaum, H., et al.: Linear and nonlinear regression techniques for simultaneous and proportional nyoelectric control. IEEE Trans. Neural Syst. Rehabil. Eng. 22(2), 269–279 (2014)
Muceli, S., Farina, D.: Simultaneous and proportional estimation of hand kinematics from EMG during mirrored movements at multiple degrees-of-freedom. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 371–378 (2012)
Kalani, H., Moghimi, S., Akbarzadeh, A.: SEMG-based prediction of masticatory kinematics in rhythmic clenching movements. Biomed. Signal Process. Control 20, 24–34 (2015)
Ison, M., Artemiadis, P.: The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control. J. Neural Eng. 11, 051001 (2014)
Clancy, E.A., Liu, L., Liu, P., Moyer, D.V.Z.: Identification of constant-posture EMG-torque relationship about the elbow using nonlinear dynamic models. IEEE Trans. Biomed. Eng. 59, 205–212 (2012)
Gijsberts, A., Bohra, R., Sierra, G.D., Werner, A., Nowak, M., Caputo, B., Roa, M.A., Castellini, C.: Stable myoelectric control of a hand prosthesis using non-linear incremental learning. Front. Neurorobot 8, 8 (2014)
d’ Avella, A., Saltiel, P., Bizzi, E.: Combinations of muscle synergies in the construction of a natural motor behavior. Nat. Neurosci. 6(3), 300–308 (2003)
d’ Avella, A., Bizzi, E.: Shared and specific muscle synergies in natural motor behaviors. Proc. Natl. Acad. Sci. U.S.A. 102, 3076–3081 (2005)
Alessandro, C., Deils, I., Nori, F., Panzeri, S., Berret, B.: Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives. Front. Comput. Neurosci. 7(43), 1 (2013)
Acknowledgement
This research has been made possible by “111 project” (Grant No. B13044), National Science Foundation of China with grant number 51475373 and Natural Science Basic Research Plan of Shaanxi Province with grant number 2016JQ6009.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Yu, W., Feng, Y., Liang, W., Wang, R., Madani, K. (2016). Estimate the Kinematics with EMG Signal Using Fuzzy Wavelet Neural Network for Biomechanical Leg Application. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-41009-8_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41008-1
Online ISBN: 978-3-319-41009-8
eBook Packages: Computer ScienceComputer Science (R0)