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Towards Estimation of Trunk Muscle Forces with a Bio-Inspired Control Strategy of Neuro-Osteoligamentous Finite Element Lumbar Spine Model

Sharifzadeh Kermani, Alireza | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53038 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Arjmand, Navid; Vossoughi, Gholamreza; Parnianpour, Mohamad
  7. Abstract:
  8. Low back pain (LBP), the leading cause of disability worldwide, remains one of the most common and challenging occupational musculoskeletal disorders. The effective assessment of LBP injury risk, and the design of appropriate treatment modalities and rehabilitation protocols, require accurate estimation of the mechanical spinal loads during different activities. This study aimed to: 1) develop a novel 2D beam-column finite element control-based model of the lumbar spine and compare its predictions for muscle forces and spinal loads to those resulting from a geometrically-matched equilibrium-based model; 2) test, using the foregoing control-based finite element model, the validity of the follower load (FL) concept suggested in the geometrically-matched model; 3) investigate the effect of change in the magnitude of the external load on trunk muscle activation patterns; and 4) inspect the effect of different muscle architectures in stabilizing the model. For the sake of the first three goals (Step1), a simple 2D continuous beam-column model of the human lumbar spine, incorporating five pairs of Hill’s muscle models, was developed in the frontal plane. Bio-inspired fuzzy neuro-controllers were used to maintain a laterally bent posture under five different external loading conditions. Muscle forces were assigned based on minimizing the kinematic error between target and actual postures while imposing a penalty on muscular activation levels. Next, intervertebral bodies and discs as well as 5 different muscle architectures under one loading condition, were considered in the model (Step2). As compared to the geometrically-matched model, our control-based model predicted similar patterns for muscle forces, but at considerably lower values (the sums of muscle forces in our model were smaller by ~ 159, 40, -11, 132, and -8 N for loading cases 1 through 5). Moreover, irrespective of the external loading conditions, a near (< 3º) optimal FL on the spine was generated by the control-based predicted muscle forces. The variation of the muscle forces with the magnitude of the external load within the simulated range at the L1 level was found linear. Results also showed efficient performance of multi-articular muscles in comparison with uni-articular and bi-articular ones. Moreover, multi-articular muscles spanning a specific level of spine devoid of any muscles, could stabilize that level. This work presents a novel methodology, based on a bio-inspired control strategy, that can be used to estimate trunk muscle forces for various clinical and occupational applications towards shedding light on the ever-elusive LBP etiology
  9. Keywords:
  10. Spine ; Controller ; Follower Load ; Stability ; Intelligent Control ; Muscle Activity ; Musculoskeletal Disorders (MSDs)

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