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Estimating the multivariable human ankle impedance in dorsi-plantarflexion and inversion-eversion directions using EMG signals and artificial neural networks

  • Houman Dallali
  • Lauren Knop
  • Leslie Castelino
  • Evandro Ficanha
  • Mohammad RastgaarEmail author
Regular Paper

Abstract

The use of a suitably designed ankle-foot prosthesis is essential for transtibial amputees to regain lost mobility. A desired ankle–foot prosthesis must be able to replicate the function of a healthy human ankle by transferring the ground reaction forces to the body, absorbing shock during contact, and providing propulsion. During the swing phase of walking, the human ankle is soft and relaxed; however, it hardens as it bears the body weight and provides force for push-off. The stiffness is one of the components of the mechanical impedance, and it varies with muscle activation (Stochastic estimation of human ankle mechanical impedance in medial-lateral direction, 2014, Stochastic estimation of the multivariable mechanical impedance of the human ankle with active muscles, 2010). This study defines the relationship between ankle impedance and the lower extremity muscle activations using artificial neural networks (ANN). We used the Anklebot, a highly backdrivable, safe, and therapeutic robot to apply stochastic position perturbations to the human ankle in the sagittal and frontal planes. A previously proposed system identification method was used to estimate the target ankle impedance to train the ANN. The ankle impedance was estimated with relaxed muscles and with lower leg muscle activations at 10 and 20% of the maximum voluntary contraction (MVC) of each individual subject. Given the root mean squared (rms) of the electromyography (EMG) signals, the proposed ANN effectively predicted the ankle impedance with mean accuracy of 89.8 ± 6.1% in DP and mean accuracy of 88.3 ± 5.7% in IE, averaged across three muscle activation levels and all subjects.

Keywords

Electromyography Ankle impedance Human ankle Artificial neural networks 

Notes

Acknowledgements

This work is supported by the National Science Foundation under CAREER grant no. 1350154. The authors would like to thank Chen Jia for his help with the preparation of this paper.

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Copyright information

© Springer Singapore 2017

Authors and Affiliations

  • Houman Dallali
    • 1
  • Lauren Knop
    • 1
  • Leslie Castelino
    • 1
  • Evandro Ficanha
    • 1
  • Mohammad Rastgaar
    • 1
    Email author
  1. 1.Department of Mechanical Engineering-Engineering MechanicsMichigan Technological UniversityHoughtonUSA

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