Journal of Intelligent & Robotic Systems

, Volume 76, Issue 3–4, pp 461–474

Improving Finite State Impedance Control of Active-Transfemoral Prosthesis Using Dempster-Shafer Based State Transition Rules

  • Ming Liu
  • Fan Zhang
  • Philip Datseris
  • He (Helen) Huang
Article

DOI: 10.1007/s10846-013-9979-3

Cite this article as:
Liu, M., Zhang, F., Datseris, P. et al. J Intell Robot Syst (2014) 76: 461. doi:10.1007/s10846-013-9979-3

Abstract

Finite state impedance (FSI) control is a widely used approach to control active-transfemoral prostheses (ATP). Current design of state transition rules depends on hard thresholding of intrinsic mechanical measurements, which cannot cope well with uncertainty related with intra- and inter-subject variations of these intrinsic recordings. In this study, we aimed to generate more robust FSI control of ATP against these variations by using Dempster-Shafer theory (DST)-based transition rules. The FSI control with DST-based rules was implemented on an instrumented ATP, evaluated on five able-bodied subjects and one patient with a unilateral transfemoral amputation. Then the DSP based transition rules were compared to the control with hard threshold (HT)-based transition rules. The results showed that when compared to the hard thresholding approach, the DST yielded enhanced accuracy in state transition timing and reduced control errors when intra- and inter-subject variations were presented. Additionally, the parameters of DST-based rules were uniform for all the subjects tested, allowing for easy and efficient transition rule design and calibration. The outcome of this study can lead to further improvement of robust, practical, and self-contained ATP design, which in turn will advance the motor function of patients with lower limb amputations.

Keywords

Impedance control Finite state machine Uncertainty Amputee gait 

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Ming Liu
    • 1
    • 2
  • Fan Zhang
    • 1
    • 2
  • Philip Datseris
    • 3
  • He (Helen) Huang
    • 1
    • 2
  1. 1.The Neuromuscular Rehabilitation Engineering Laboratory, Department of Electrical, Computer, and Biomedical EngineeringUniversity of Rhode IslandKingstonUSA
  2. 2.The Neuromuscular Rehabilitation Engineering Laboratory, Department of Biomedical EngineeringNorth Carolina State UniversityRaleighUSA
  3. 3.Department of Mechanical, Industrial and Systems EngineeringUniversity of Rhode IslandKingstonUSA

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