A Cyber Expert System for Auto-Tuning Powered Prosthesis Impedance Control Parameters

Abstract

Typically impedance control parameters (e.g., stiffness and damping) in powered lower limb prostheses are fine-tuned by human experts (HMEs), which is time and resource intensive. Automated tuning procedures would make powered prostheses more practical for clinical use. In this study, we developed a novel cyber expert system (CES) that encoded HME tuning decisions as computer rules to auto-tune control parameters for a powered knee (passive ankle) prosthesis. The tuning performance of CES was preliminarily quantified on two able-bodied subjects and two transfemoral amputees. After CES and HME tuning, we observed normative prosthetic knee kinematics and improved or slightly improved gait symmetry and step width within each subject. Compared to HME, the CES tuning procedure required less time and no human intervention. Hence, using CES for auto-tuning prosthesis control was a sound concept, promising to enhance the practical value of powered prosthetic legs. However, the tuning goals of CES might not fully capture those of the HME. This was because we observed that HME tuning reduced trunk sway, while CES sometimes led to slightly increased trunk motion. Additional research is still needed to identify more appropriate tuning objectives for powered prosthetic legs to improve amputees’ walking function.

This is a preview of subscription content, access via your institution.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

Abbreviations

k :

Stiffness

θ E :

Equilibrium position

C :

Damping coefficient

θ p :

Prosthesis knee joint angle

\(\dot{\theta }_{\text{p}}\) :

Prosthesis knee joint angular velocity

θ peak :

Peak knee angle

T dura :

Gait phase duration

\(\dot{\theta }_{\text{peak}}\) :

Peak angular velocity

m :

Membership function value

D :

Rule degree

N:

Negative

P:

Positive

CES:

Cyber expert system

HME:

Human expert

CV:

Coefficient of variation

DF:

Statistical degrees of freedom

GRF:

Ground reaction force

IC:

Impedance control

IDS:

Initial double support

PKP:

Powered knee prosthesis

RMS:

Root-mean-square

SI:

Symmetry index

SS:

Single support

SWE:

Swing extension

SWF:

Swing flexion

TDS:

Terminal double support

References

  1. 1.

    Badiru, A. B., and J. Cheung. Fuzzy Engineering Expert Systems with Neural Network Applications. New York: Wiley, 2002.

    Google Scholar 

  2. 2.

    Borjian, R., J. Lim, M. B. Khamesee and W. Melek. The design of an intelligent mechanical active prosthetic knee. In Proceedings of 34th Conference on IEEE Industrial Electronics Society, 2008, pp. 2918–2923.

  3. 3.

    Dingwell, J. B., and L. C. Marin. Kinematic variability and local dynamic stability of upper body motions when walking at different speeds. J. Biomech. 39:444–452, 2006.

    Article  PubMed  Google Scholar 

  4. 4.

    Fletcher, D. D., K. L. Andrews, J. W. Hallett, Jr, M. A. Butters, C. M. Rowland, and S. J. Jacobsen. Trends in rehabilitation after amputation for geriatric patients with vascular disease: implications for future health resource allocation. Arch. Phys. Med. Rehabil. 83:1389–1393, 2002.

    Article  PubMed  Google Scholar 

  5. 5.

    Giannini, S. Gait Analysis: Methodologies and Clinical Applications. Amsterdam: IOS Press for Bioengineering Technical & Systems, 1994.

    Google Scholar 

  6. 6.

    Granata, K. P., M. F. Abel, and D. L. Damiano. Joint angular velocity in spastic gait and the influence of muscle-tendon lengthening. J. Bone Joint Surg. Am. 82A:174–186, 2000.

    Google Scholar 

  7. 7.

    Gregg, R. D., T. Lenzi, L. J. Hargrove, and J. W. Sensinger. Virtual constraint control of a powered prosthetic leg: from simulation to experiments with transfemoral amputees. IEEE Trans. Robot. 30:1455–1471, 2014.

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Gregg, R. D., T. Lenzi, N. P. Fey, L. J. Hargrove and J. W. Sensinger. Experimental effective shape control of a powered transfemoral prosthesis. In IEEE International Conference on Rehabilitation Robotics, 2013.

  9. 9.

    Guillaume, S. Designing fuzzy inference systems from data: an interpretability—oriented review. IEEE Trans. Fuzzy Syst. 9:426–443, 2001.

    Article  Google Scholar 

  10. 10.

    Herr, H., and A. Wilkenfeld. User-adaptive control of a magnetorheological prosthetic knee. Ind. Robot 30:42–55, 2003.

    Article  Google Scholar 

  11. 11.

    Holgate, M. A., T. G. Sugar and A. W. Bohler. A novel control algorithm for wearable robotics using phase plane invariants. In Proceedings of IEEE International Conference on Robot Automation, 2009, pp. 3845–3850.

  12. 12.

    Huang, S.-J., and J.-S. Lee. A stable self-organizing fuzzy controller for robotic motion control. IEEE Trans. Ind. Electron. 47:421–428, 2000.

    Article  Google Scholar 

  13. 13.

    Kadaba, M. P., H. K. Ramakrishnan, M. E. Wootten, J. Gainey, G. Gorton, and G. V. Cochran. Repeatability of kinematic, kinetic, and electromyographic data in normal adult gait. J. Orthop. Res. 7:849–860, 1989.

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Kearney, R. E., and I. W. Hunter. System-identification of human joint dynamics. Crit. Rev. Biomed. Eng. 18:55–87, 1990.

    CAS  PubMed  Google Scholar 

  15. 15.

    Lambrecht, B. G. A. and H. Kazerooni. Design of a Semi-Active Knee Prosthesis. In IEEE International Conference on Robotics, 2009, pp. 4097–4103.

  16. 16.

    Lawson, B. E., B. Ruhe, A. Shultz, and M. Goldfarb. A powered prosthetic intervention for bilateral transfemoral amputees. IEEE Trans. Biomed. Eng. 62:1042–1050, 2015.

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Lenzi, T., L. J. Hargrove and J. W. Sensinger. Minimum jerk swing control allows variable cadence in powered transfemoral prostheses. In Conference Proceedings IEEE Engineering in Medicine and Biology Society, 2014, pp. 2492–2495.

  18. 18.

    Liu, M., P. Datseris and H. Huang. A prototype for smart prosthetic legs: analysis and mechanical design. In Proceedings of International Conference on Control Robotics Cybernetics, 2011, pp. 139–143.

  19. 19.

    Liu, M., F. Zhang, P. Datseris, and H. Huang. Improving finite state impedance control of active transfemoral prostheses using Dempster-Shafer state transition rules. J. Intell. Robot. Syst. 76:461–474, 2014.

    Article  Google Scholar 

  20. 20.

    Martinez-Vilialpando, E. C., and H. Herr. Agonist-antagonist active knee prosthesis: a preliminary study in level-ground walking. J. Rehabil. Res. Dev. 46:361–373, 2009.

    Article  Google Scholar 

  21. 21.

    McAndrew Young, P. M., and J. B. Dingwell. Voluntarily changing step length or step width affects dynamic stability of human walking. Gait Posture 35:472–477, 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Melingui, A., C. R. Merzouki and J. B. Mbede. Adaptive navigation of an omni-drive autonomous mobile robot in unstructured dynamic environments. In Proceedings of IEEE International Conference on Robot Biomimetics, 2013, pp. 1924–1929.

  23. 23.

    Miller, W. C., A. B. Deathe, M. Speechley, and J. Koval. The influence of falling, fear of falling, and balance confidence on prosthetic mobility and social activity among individuals with a lower extremity amputation. Arch. Phys. Med. Rehabil. 82:1238–1244, 2001.

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Mirbagheri, M. M., H. Barbeau, and R. E. Kearney. Intrinsic and reflex contributions to human ankle stiffness: variation with activation level and position. Exp. Brain Res. 135:423–436, 2000.

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Negnevitsky, M. Artificial Intelligence: A Guide to Intelligent Systems. Essex: Pearson Education, 2005.

    Google Scholar 

  26. 26.

    Nolan, L., A. Wit, K. Dudzinski, A. Lees, M. Lake, and M. Wychowanski. Adjustments in gait symmetry with walking speed in trans-femoral and trans-tibial amputees. Gait Posture 17:142–151, 2003.

    Article  PubMed  Google Scholar 

  27. 27.

    Orendurff, M. S., A. D. Segal, G. K. Klute, J. S. Berge, E. S. Rohr, and N. J. Kadel. The effect of walking speed on center of mass displacement. J. Rehabil. Res. Dev. 41:829–834, 2004.

    Article  PubMed  Google Scholar 

  28. 28.

    Perry, J. Gait Analysis: Normal and Pathological Function. Thorofare: SLACK Inc., 1992.

    Google Scholar 

  29. 29.

    Pfeifer, S., R. Riener and H. Vallery. Knee stiffness estimation in physiological gait. In Conference Proceedings IEEE Engineering in Medicine and Biology Society, 2014, pp. 1607–1610.

  30. 30.

    Pfeifer, S., H. Vallery, M. Hardegger, R. Riener, and E. J. Perreault. Model-based estimation of knee stiffness. IEEE Trans. Biomed. Eng. 59:2604–2612, 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Rouse, E. J., L. J. Hargrove, E. J. Perreault, and T. A. Kuiken. Estimation of human ankle impedance during the stance phase of walking. IEEE Trans. Neural Syst. Rehabil. Eng. 22:870–878, 2014.

    Article  PubMed  Google Scholar 

  32. 32.

    Shamaei, K., G. S. Sawicki, and A. M. Dollar. Estimation of quasi-stiffness and propulsive work of the human ankle in the stance phase of walking. PLoS One 8:e59935, 2013.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Shamaei, K., G. S. Sawicki, and A. M. Dollar. Estimation of quasi-stiffness of the human hip in the stance phase of walking. PLoS One 8:e81841, 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Shamaei, K., G. S. Sawicki, and A. M. Dollar. Estimation of quasi-stiffness of the human knee in the stance phase of walking. PLoS One 8:e59993, 2013.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Siler, W., and J. J. Buckley. Fuzzy Expert Systems and Fuzzy Reasoning. Hoboken: Wiley, 2005.

    Google Scholar 

  36. 36.

    Simon, A. M., K. A. Ingraham, N. P. Fey, S. B. Finucane, R. D. Lipschutz, A. J. Young, and L. J. Hargrove. Configuring a powered knee and ankle prosthesis for transfemoral amputees within five specific ambulation modes. PLoS One 9:e99387, 2014.

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Sup, F., A. Bohara, and M. Goldfarb. Design and control of a powered transfemoral prosthesis. Int. J. Robot. Res. 27:263–273, 2008.

    Article  Google Scholar 

  38. 38.

    Sup, F., H. A. Varol, J. Mitchell, T. J. Withrow, and M. Goldfarb. Preliminary evaluations of a self-contained anthropomorphic transfemoral prosthesis. IEEE/ASME Trans. Mechatron. 14:667–676, 2009.

    Article  Google Scholar 

  39. 39.

    Torrealba, R. R., G. Fernandez-Lopez, and J. C. Grieco. Towards the development of knee prostheses: review of current researches. Kybernetes 37:1561–1576, 2008.

    Article  Google Scholar 

  40. 40.

    Tura, A., M. Raggi, L. Rocchi, A. G. Cutti, and L. Chiari. Gait symmetry and regularity in transfemoral amputees assessed by trunk accelerations. J. Neuroeng. Rehabil. 7:4, 2010.

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Wang, L. X., and J. M. Mendel. Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 22:1414–1427, 1992.

    Article  Google Scholar 

  42. 42.

    Weiss, P. L., R. E. Kearney, and I. W. Hunter. Position dependence of ankle joint dynamics—I. Passive Mech. 19:727–735, 1986.

    CAS  Google Scholar 

  43. 43.

    Weiss, P. L., R. E. Kearney, and I. W. Hunter. Position dependence of ankle joint dynamics—II. Active Mech. 19:737–751, 1986.

    CAS  Google Scholar 

  44. 44.

    Winter, D. A. Kinematic and kinetic patterns in human gait: variability and compensating effects. Hum. Mov. Sci. 3:51–76, 1984.

    Article  Google Scholar 

  45. 45.

    Winter, D. A. The Biomechanics and Motor Control of Human Gait. Waterloo: University of Waterloo Press, 1987.

    Google Scholar 

  46. 46.

    Ziegler-Graham, K., E. J. MacKenzie, P. L. Ephraim, T. G. Travison, and R. Brookmeyer. Estimating the prevalence of limb loss in the United States: 2005 to 2050. Arch. Phys. Med. Rehabil. 89:422–429, 2008.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Derek Frankena and Stephen Harper of Atlantic Prosthetics and Orthotics and Michael Astilla of Bio-Tech Prosthetics and Orthotics for fitting our study participants with prosthetic adaptors and sockets. We also thank William Boatwright for assisting with data collection and Andrea Brandt, Dr. Jon Stallings, and Dr. Consuelo Arellano for assisting with statistical analysis. This work was partly funded by a seed grant from the UNC/NCSU Rehabilitation Engineering Core, and by the National Science Foundation (NSF #1361549, 1406750, and 1527202).

Conflict of interest

No benefits in any form have been or will be received from a commercial party related directly or indirectly to the subject of this manuscript.

Author information

Affiliations

Authors

Corresponding author

Correspondence to He Huang.

Additional information

Associate Editor Michael Torry oversaw the review of this article.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 151 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Huang, H., Crouch, D.L., Liu, M. et al. A Cyber Expert System for Auto-Tuning Powered Prosthesis Impedance Control Parameters. Ann Biomed Eng 44, 1613–1624 (2016). https://doi.org/10.1007/s10439-015-1464-7

Download citation

Keywords

  • Powered prosthetic legs
  • Biomechanics
  • Gait
  • Expert system
  • Calibration
  • Transfemoral amputation