Prediction of muscle activation for an eye movement with finite element modeling. (1st October 2017)

• Record Type: Journal Article
• Authors: Karami, Abbas; Eghtesad, Mohammad; Haghpanah, Seyyed Arash
• Is Part Of: Computers in biology and medicine. Volume 89(2017)
• Page Start: 368
• Page End: 378
• Publication Date: 2017-10-01
• Subjects: Eye orbit -- Forward dynamics simulation -- Tracking-based inverse controller -- Finite element method -- Extraocular muscle modeling -- Activation prediction; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.08.018
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: In this paper, a 3D finite element (FE) modeling is employed in order to predict extraocular muscles' activation and investigate force coordination in various motions of the eye orbit. A continuum constitutive hyperelastic model is employed for material description in dynamic modeling of the extraocular muscles (EOMs). Two significant features of this model are accurate mass modeling with FE method and stimulating EOMs for motion through muscle activation parameter. In order to validate the eye model, a forward dynamics simulation of the eye motion is carried out by variation of the muscle activation. Furthermore, to realize muscle activation prediction in various eye motions, two different tracking-based inverse controllers are proposed. The performance of these two inverse controllers is investigated according to their resulted muscle force magnitude and muscle force coordination. The simulation results are compared with the available experimental data and the well-known existing neurological laws. The comparison authenticates both the validation and the prediction results. Highlights: A 3D dynamic finite element model of eye is proposed by considering active-passive hyper–elastic muscle. The eye model is validated through comparing muscle force and eye trajectory in 15-degree saccade motion in experiment with simulation. Automatic activation prediction is accomplished for different eye motions by two different control approach and various gains. Proper activation prediction scheme is found for eye motion.