Annals of Biomedical Engineering

, Volume 44, Issue 5, pp 1613–1624 | Cite as

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

  • He HuangEmail author
  • Dustin L. Crouch
  • Ming Liu
  • Gregory S. Sawicki
  • Ding Wang


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.


Powered prosthetic legs Biomechanics Gait Expert system Calibration Transfemoral amputation 

List of symbols




Equilibrium position


Damping coefficient


Prosthesis knee joint angle

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

Prosthesis knee joint angular velocity


Peak knee angle


Gait phase duration

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

Peak angular velocity


Membership function value


Rule degree






Cyber expert system


Human expert


Coefficient of variation


Statistical degrees of freedom


Ground reaction force


Impedance control


Initial double support


Powered knee prosthesis




Symmetry index


Single support


Swing extension


Swing flexion


Terminal double support



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.

Supplementary material

10439_2015_1464_MOESM1_ESM.pdf (151 kb)
Supplementary material 1 (PDF 151 kb)


  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.Google Scholar
  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.CrossRefPubMedGoogle 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.CrossRefPubMedGoogle 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.CrossRefPubMedPubMedCentralGoogle 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.Google Scholar
  9. 9.
    Guillaume, S. Designing fuzzy inference systems from data: an interpretability—oriented review. IEEE Trans. Fuzzy Syst. 9:426–443, 2001.CrossRefGoogle Scholar
  10. 10.
    Herr, H., and A. Wilkenfeld. User-adaptive control of a magnetorheological prosthetic knee. Ind. Robot 30:42–55, 2003.CrossRefGoogle 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.Google Scholar
  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.CrossRefGoogle 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.CrossRefPubMedGoogle Scholar
  14. 14.
    Kearney, R. E., and I. W. Hunter. System-identification of human joint dynamics. Crit. Rev. Biomed. Eng. 18:55–87, 1990.PubMedGoogle 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.Google Scholar
  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.CrossRefPubMedPubMedCentralGoogle 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.Google Scholar
  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.Google Scholar
  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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefPubMedPubMedCentralGoogle 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.Google Scholar
  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.CrossRefPubMedGoogle 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.CrossRefPubMedGoogle 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.CrossRefPubMedGoogle 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.CrossRefPubMedGoogle 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.Google Scholar
  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.CrossRefPubMedPubMedCentralGoogle 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.CrossRefPubMedGoogle 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.CrossRefPubMedPubMedCentralGoogle 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.CrossRefPubMedPubMedCentralGoogle 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.CrossRefPubMedPubMedCentralGoogle 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.CrossRefPubMedPubMedCentralGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefPubMedPubMedCentralGoogle 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.CrossRefGoogle 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.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.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.CrossRefGoogle 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.CrossRefPubMedGoogle Scholar

Copyright information

© Biomedical Engineering Society 2015

Authors and Affiliations

  • He Huang
    • 1
    • 2
    Email author
  • Dustin L. Crouch
    • 1
    • 2
  • Ming Liu
    • 1
    • 2
  • Gregory S. Sawicki
    • 1
    • 2
  • Ding Wang
    • 1
    • 2
  1. 1.UNC/NCSU Joint Department of Biomedical EngineeringNorth Carolina State UniversityRaleighUSA
  2. 2.UNC/NCSU Joint Department of Biomedical EngineeringUniversity of North Carolina at Chapel HillChapel HillUSA

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