A Hybrid Neuro-fuzzy Approach for Spinal Force Evaluation in Manual Materials Handling Tasks

  • Yanfeng Hou
  • Jacek M. Zurada
  • Waldemar Karwowski
  • William S. Marras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3612)

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    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)CrossRefMathSciNetGoogle Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    Wang, Y.C., Zipser, D.: A learning algorithm for continually running recurrent neural networks. Neural Comput. 1(2), 270–280 (1989)CrossRefGoogle Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    Hou, Y., Zurada, J.M., Karwowski, W.: Identification of Input Variables using Fuzzy Average with Fuzzy Cluster Distribution. Submitted to IEEE Trans. on Fuzzy SystemsGoogle Scholar
  21. 21.
    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)CrossRefGoogle Scholar
  22. 22.
    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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yanfeng Hou
    • 1
  • Jacek M. Zurada
    • 1
  • Waldemar Karwowski
    • 2
  • William S. Marras
    • 3
  1. 1.Department of Electrical and Computer EngineeringUniversity of Louisville 
  2. 2.Department of Industrial EngineeringUniversity of Louisville 
  3. 3.Biodynamics Laboratory, Institute for ErgonomicsThe Ohio State University 

Personalised recommendations