Abstract
Evaluation of the spinal forces from kinematics data is very complicated because it involves the handling of relationship between kinematic variables and electromyography (EMG) responses, as well as the relationship between EMG responses and the forces. A recurrent fuzzy neural network (RFNN) model is proposed to establish the kinematics-EMG-force relationship and model the dynamics of muscular activities. The EMG signals are used as an intermediate output and are fed back to the input layer. Since the EMG signal is a direct reflection of muscular activities, the feedback of this model has a physical meaning. It expresses the dynamics of muscular activities in a straightforward way and takes advantage from the recurrent property. The trained model can then have the forces predicted directly from kinematic variables while bypassing the procedure of measuring EMG signals and avoiding the use of biomechanics model. A learning algorithm is derived for the RFNN.
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References
Lloyd, D.G., Besier, T.F.: An EMG-driven Musculoskeletal Model to Estimate Muscle Forces and Knee Joint Moments in Vivo. Journal of Biomechanics 36, 765–776 (2003)
Crosby, P.A.: Use of surface electromyogram as a measure of dynamic force in human limb muscles. Med. and Biol. Eng. and Comput. 16, 519–524 (1978)
Wang, L., Buchanan, T.S.: Prediction of Joint Moments Using a Neural Network Model of Muscle Activations from EMG Signals. IEEE Trans. on Neural Systems and Rehabilitation Engineering 10(1), 30–37 (2002)
Luh, J.J., Chang, G.C., Cheng, C.K., Lai, J.S., Kuo, T.S.: Isokinetic elbow joint torques estimation form surface EMG and joint kinematic data: Using an artificial neural network model. J. Electromyogr. Kinesiol. l(9), 173–183 (1999)
Liu, M.M., Herzog, W., Savelberg, H.H.: Dynamic muscle force predictions from EMG: An artificial neural network approach. J. Electromyogr. Kinesiol. 9, 391–400 (1999)
Hussein, S.E., Granat, M.H.: Intention detection using a neuro-fuzzy EMG classifier. Engineering in Medicine and Biology Magazine, IEEE 21(6), 123–129 (November-December)
Kiguchi, K., Tanaka, T., Fukuda, T.: Neuro-fuzzy control of a robotic exoskeleton with EMG signals. IEEE Transactions on Fuzzy Systems 12(4), 481–490 (2004)
Hou, Y., Zurada, J.M., Karwowski, W.: Prediction of EMG Signals of Trunk Muscles in Manual Lifting Using a Neural Network Model. In: Proc. of the Int. Joint Conf. on Neural Networks, July 25-29, pp. 1935–1940 (2004)
Hou, Y., Zurada, J.M., Karwowski, W.: Prediction of Dynamic Forces on Lumbar Joint Using a Recurrent Neural Network Model. In: Proc. of the 2004 Int. Conf. on Machine Learning and Applications (ICMLA 2004), December 16-18, pp. 360–365 (2004)
Wu, S., Er, M.J.: Dynamic fuzzy neural networks-a novel approach to function approximation. IEEE Trans. on Systems, Man and Cybernetics B 30, 358–364 (2000)
Wu, S., Er, M.J., Gao, Y.: A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans. on Fuzzy Systems 9, 578–594 (2001)
Juang, C.F., Lin, C.T.: An on-line self-constructing neural fuzzy inference network and its applications. IEEE Trans. on Fuzzy Systems 6, 12–32 (1998)
Lee, C.H., Teng, C.C.: Identification and control of dynamic systems using recurrent fuzzy neural networks. IEEE Trans. on Fuzzy Systems 8(4), 349–366 (2000)
Lin, C.M., Hsu, C.F.: Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings. IEEE Trans. on Fuzzy Systems 12(5), 733–742 (2004)
Juang, C.F.: A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms. IEEE Trans. on Fuzzy Systems 10(2), 155–170 (2002)
Lin, F.J., Wai, R.J.: Hybrid control using recurrent fuzzy neural network for linear-induction motor servo drive. IEEE Trans. on Fuzzy Systems 9(1), 68–90 (2001)
Lin, F.J., Wai, R.J., Hong, C.M.: Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic inputs. IEEE Trans. on Neural Networks 12(1), 102–115 (2001)
Wang, Y.C., Zipser, D.: A learning algorithm for continually running recurrent neural networks. Neural Comput. 1(2), 270–280 (1989)
Williams, R.J., Chien, C.J., Teng, C.C.: Direct adaptive iterative learning control of nonlinear systems using an output-recurrent fuzzy neural network. IEEE Trans. on Systems, Man, and Sybernetics 34(3), 1348–1359 (2004)
Hou, Y., Zurada, J.M., Karwowski, W.: Identification of Input Variables using Fuzzy Average with Fuzzy Cluster Distribution. Submitted to IEEE Trans. on Fuzzy Systems
Auephanwiriyakul, S., Keller, J.M.: Analysis and efficient implementation of a linguistic fuzzy c-means. IEEE Trans. on Fuzzy Systems 10(5), 563–582 (2002)
Lee, W., Karwowski, W., Marras, W.S., Rodrick, D.: A neuro-fuzzy model for estimating electromyographical activity of trunk muscles due to manual lifting. Ergonomics 46(1-3), 285–309 (2003)
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Hou, Y., Zurada, J.M., Karwowski, W., Marras, W.S. (2005). A Hybrid Neuro-fuzzy Approach for Spinal Force Evaluation in Manual Materials Handling Tasks. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_154
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DOI: https://doi.org/10.1007/11539902_154
Publisher Name: Springer, Berlin, Heidelberg
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