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Estimation of Human Elbow Joint Mechanical Transfer Function during Steady State and during Cyclical Movements

  • Gideon F. Inbar

Abstract

Signal processing techniques can be beneficially used in studies of system model identification and model parameter estimation, and as powerful tools in elucidating the structure and function of partially known biological systems. In the present study, pseudorandom band limited white noise mechanical perturbations are used in order to investigate the torque to angular displacement transfer function (TF) of the human elbow joint.

Very high coherence was obtained for the steady state mechanical TF measurements. The mechanical parameters were also estimated during cyclic movements at different cycle times and different loads. A one or two zeroes and two poles ARMA model gave a clear best fit for more than 90% of the data with the model parameters changing with the operating point and with the level of muscle force. Thus, a linear second order model with changing parameters appears to describe well the single degree of freedom (DOF) elbow joint. The model stiffness increased with movement acceleration and inertial load, i.e., with joint torque. Increased stiffness, and with it, damping at the cyclic target points, is a desirable condition to achieve zero velocity at these points. During the high velocity low acceleration portion of the cycle the stiffness could be lower than under static conditions. Models that estimate equilibrium trajectories based on high joint stiffness during movement must therefore be reassessed.

Keywords

Elbow Joint Joint Torque Cyclical Movement Torque Input Final Prediction Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Aaron, A., and Inbar. G., 1995, Elbow joint movement control: An experimental and theoretical study, The French Israeli Symposium on Robotics, May 22–23, Herzelia, Israel. Submitted for publication.Google Scholar
  2. Allin J.. and Inbar, G., 1986, FNS parameter selection and upper limb characterization, IEEE Trans on Biomed. Eng. BME-33(9): 809–817.Google Scholar
  3. Agarwal, G. C., and Gottlieb, G. L.. 1977, Compliance of human ankle joint, J. Biomechan. Eng. 99: 166–170.CrossRefGoogle Scholar
  4. Bennet, D. J., 1990. The control of human arm movement: models and mechanical constraints. Ph.D. Thesis, MIT, Dept. of Brain and Cognitive Sci.Google Scholar
  5. Bennet, D. J., Hollerbach, J. M., Xu, Y., and Hunter, I. W., 1992, Time-varying stiffness of human elbow joint during cyclic voluntary movement, Exp. Brain Res. 88: 433–442.CrossRefGoogle Scholar
  6. Feldman, A. G., 1986, Once more on the equilibrium-point hypothesis (2L model) for motor control, J. Motor Behavior 18 (1): 17–54.Google Scholar
  7. Flash, T., 1987, The control of hand equilibrium trajectories in multi-joint arm movements, Biol. Cybernetics 57: 257–274.zbMATHCrossRefGoogle Scholar
  8. Hoffer, J. A., and Andreassen, S., 1981, Regulation of soleus muscle stiffness in premammillary cats: intrinsic and reflex components, J. Neurophysiol. 45: 267–285.Google Scholar
  9. Hogan, N., 1985, Impedance control, An approach to manipulation. Part i–Theory; Part 2–Implementation; Part 3–Application, J. Dynamic System Measurement and Control 107: 1–23.zbMATHCrossRefGoogle Scholar
  10. Hunter, I. W., and Kearney, R. E., 1982, Dynamics of human ankle stiffness: Variation with mean ankle torque, J. Biomechan. 15 (10): 747–752.CrossRefGoogle Scholar
  11. Inbar, G.F., Baskin, R., and Hsia, S., 1970, Parameter identification analysis of muscle dynamics. Mathematical Biosciences 7: 61–79.zbMATHCrossRefGoogle Scholar
  12. Inbar, G. F., and Yafe, A., 1976, Parameter and signal adaptation in the stretch reflex loop, Prog. in Brain Res. 44:317–337.Google Scholar
  13. Latash, M., 1993. Control of Human Movement, Human Kinetics Publishers, Illinois.Google Scholar
  14. MacNeil, J. B., Kearney, R. E., and Hunter, E. W., 1992, Identification of time varying biological systems from ensemble data, IEEE Trans. on Biomed. Eng. 39 (12): 1213–1224.CrossRefGoogle Scholar
  15. McIntyre, J., and Bizzi. E., 1993, Servo hypotheses for the biological control of movement, J. Motor Behavior 25 (3): 193–202.MathSciNetCrossRefGoogle Scholar
  16. McRuer, D. T., Magdeleno, R. E., and G. P. Moore, 1968, A neuromuscular actuation system model. IEEE Trans. on Man-Machine Systems MMS-9(3): 61–71.Google Scholar
  17. Paiss, O., and Inbar, G.F., 1987, AR model representation of surface EMG and its application to fatigue measurements. IEEE Trans. on Biomed. Eng. 34: 761–770.CrossRefGoogle Scholar
  18. Porat, B., 1994, Digital Processing of Random Signals - Theory & Models, Prentice-Hall Inc., Englewood Cliffs, NJ.Google Scholar
  19. van den Bosch, P. P. J., and van der Klauw, A. C., 1994, Modeling Identification and Simulation of Dynamical Systems, CRC Press, Boca Raton.zbMATHGoogle Scholar
  20. van Sonderen, J.F., and van der Gon, J.J.D., 1990. A simulation study of a programmed fast two-joint arm movements: Responses to single-and double-step target displacemtns. Biol. Cvhern. 63: 35–44.CrossRefGoogle Scholar
  21. Weissman, S., 1995, Identification of elbow joint time varying parameters. MSc. Dissertation, Dept. of Electrical Eng., Technion, Haifa.Google Scholar
  22. Yosef, V. A., and Inbar, G. F., 1986, Parameter estimation of the mechanical impedance of the forearm of the human-operator using Gaussian torque input, March, EE Pub. No. 582, Technion-lsrael Institute of Technology, Haifa.Google Scholar

Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Gideon F. Inbar
    • 1
  1. 1.Department of Electrical EngineeringTechnion-IITHaifaIsrael

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