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EMG-to-force estimation with full-scale physiology based muscle model

Mitsuhiro Hayashibe 1, * David Guiraud 1, 2 Philippe Poignet 1 
* Corresponding author
1 DEMAR - Artificial movement and gait restoration
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : EMG-to-force estimation for voluntary muscle contraction has many applications in human-machine interaction, motion analysis, and rehabilitation robotics for prosthetic limbs or exoskeletons. EMG-based model can account for a subject's individual activation patterns to estimate muscle force. For the estimation, so-called Hill-type model has been used in most of the cases. It already has shown its promising performance, but it is still known as a phenomenological model considering only macroscopic physiology. We have already developed the physiological based muscle model for the use of functional electrical stimulation (FES) which can render the myoelectrical property also in microscopic scale. In this paper we discuss EMG-to-force estimation based on this full physiological based muscle model in voluntary contraction. In addition to Hill macroscopic structure, a microscopic physiology originally designed by Huxley is integrated. It has significant meaning to realize the same kind of EMG-to-force estimation with a physiological based model not with a phenomenological model, because it brings the understanding of the internal biophysical dynamics and new insights about neuromuscular activations. Using same EMG data of isometric muscle contraction, the force estimation results are shown by classical approach and new physiological based approach. Its interpretation is also discussed.
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Submitted on : Tuesday, November 3, 2009 - 4:04:04 PM
Last modification on : Friday, August 5, 2022 - 10:47:19 AM
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Mitsuhiro Hayashibe, David Guiraud, Philippe Poignet. EMG-to-force estimation with full-scale physiology based muscle model. IROS'09: International Conference on Intelligent RObots and Systems, Oct 2009, St. Louis, MO, United States. pp.1621-1626, ⟨10.1109/IROS.2009.5354644⟩. ⟨lirmm-00429594⟩



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