Using lower extremity muscle activity to obtain human ankle impedance in the external–internal direction
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The human ankle has a critical role in locomotion and estimating its impedance is essential for human gait rehabilitation. The ankle is the first major joint that regulates the contact forces between the human body and the environment, absorbing shocks during the stance, and providing propulsion during walking. Its impedance varies with the level of the muscle activation. Hence, characterizing the complex relation between the ankle impedance and the lower leg’s muscle activation levels may improve our understanding of the neuromuscular characteristics of the ankle. Most ankle–foot prostheses do not have a degree of freedom in the transverse plane, which can cause high amounts of shear stress to be applied to the socket and can lead to secondary injuries. Quantifying the ankle impedance in the transverse plane can guide the design for a variable impedance ankle–foot prosthesis that can significantly reduce the shear stress on the socket. This paper presents the results of applying artificial neural networks (ANN) to learn and estimate the relation between the ankle impedance in the transverse plane under non-load bearing condition using electromyography signals (EMG) from the lower leg muscles. The Anklebot was used to apply pseudorandom perturbations to the human ankle in the transverse plane while the other degrees of freedom (DOF) in the sagittal and frontal planes were constrained. The mechanical impedance of the ankle was estimated using a previously proposed stochastic identification method that describes the ankle impedance as a function of the applied disturbances torques and the ankle motion output. The ankle impedance with relaxed muscles and with the lower leg’s muscle activations at 10 and 20% of the maximum voluntary contraction were estimated. The proposed ANN effectively predicts the ankle impedance within 85% accuracy (±5 Nm/rad absolute) for nine out of ten subjects given the root-mean-squared (rms) of the EMG signals. The main contribution of this paper is to quantify the relationship between lower leg muscle EMG signals and the ankle impedance in the transverse plane to pave the way towards designing and controlling this degree of freedom in a future ankle–foot prosthesis.
KeywordsElectromyography (EMG) Ankle impedance Human ankle Artificial neural networks
This material is based upon work supported by the National Science Foundation under CAREER Grant no. 1350154.
- Basmajian, J.V.: Muscles alive, their functions revealed by electromyography, 4th edn. Williams & Wilkins, Baltimore (1979)Google Scholar
- Ficanha, E.M., Rastgaar, M.: Stochastic estimation of human ankle mechanical impedance in lateral/medial direction. In: ASME Dynamic Systems and Control Conference (DSCC), San Antonio (2014)Google Scholar
- Gopura, R.A.R.C., Bandara, D.S.V., Gunasekara, J.M.P., Jayawardane, T.S.S.: Recent trends in EMG-based control methods for assistive robots. In: Electrodiagnosis in New Frontiers of Clinical Research, pp. 237–268 (2013)Google Scholar
- Karsoliya, S.: Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. Int. J. Eng. Trends Technol. 3, 714–717 (2012)Google Scholar
- Kearney, R.E., Hunter, I.W.: System identification of human joint dynamics. Crit. Rev. Biomed. Eng. 18, 55–87 (1990)Google Scholar
- Rastgaar, M., Ho, P., Lee, H., Krebs, H.I., Hogan, N.: Stochastic estimation of multi-variable human ankle mechanical impedance. In: ASME Dynamic Systems and Control Conference, Hollywood (2009)Google Scholar
- Rastgaar, M., Ho, P., Lee, H., Krebs, H.I., Hogan, N.: Stochastic estimation of the multi-variable mechanical impedance of the human ankle with active muscles. In: ASME Dynamic Systems and Control Conference, Boston (2010)Google Scholar
- Rastgaar, M., Lee, H., Ficanha, E.M., Ho, P., Krebs, H.I., Hogan, N.: Multi-directional dynamic mechanical impedance of the human ankle; a key to anthropomorphism in lower extremity assistive robots. In: Neuro-Robotics: From Brain Machine Interfaces to Rehabilitation Robotics, pp. 85–103. Springer, New York (2014)Google Scholar