A computationally efficient strategy to estimate muscle forces in a finite element musculoskeletal model of the lower limb |
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| Affiliation: | 1. Department of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL, USA;2. Department of Mechanical Engineering, Rice University, Houston, TX, USA;1. State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi''an Jiaotong University, 710054 Xi''an, Shaanxi, China;2. Department of Arthroplasty Surgery, the Second Affiliated Hospital of Inner Mongolia Medical University, 010030 Hohhot, Inner Mongolia, China;3. Department of Orthopedics, the Affiliated Hospital of Inner Mongolia Medical University, 010050 Hohhot, Inner Mongolia, China;4. Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, UK;1. School of Science and Technology, Middlesex University, London, UK;2. Department of Biomechanical Engineering, University of Twente, the Netherlands;3. Orthopaedic Research Laboratory, Radboud Institute for Health Sciences, Radboud University Medical Center, the Netherlands;4. Department of Engineering Mechanics, Dalian University of Technology, Dalian, China |
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| Abstract: | Concurrent multiscale simulation strategies are required in computational biomechanics to study the interdependence between body scales. However, detailed finite element models rarely include muscle recruitment due to the computational burden of both the finite element method and the optimization strategies widely used to estimate muscle forces. The aim of this study was twofold: first, to develop a computationally efficient muscle force prediction strategy based on proportional-integral-derivative (PID) controllers to track gait and chair rise experimental joint motion with a finite element musculoskeletal model of the lower limb, including a deformable knee representation with 12 degrees of freedom; and, second, to demonstrate that the inclusion of joint-level deformability affects muscle force estimation by using two different knee models and comparing muscle forces between the two solutions. The PID control strategy tracked experimental hip, knee, and ankle flexion/extension with root mean square errors below 1°, and estimated muscle, contact and ligament forces in good agreement with previous results and electromyography signals. Differences up to 11% and 20% in the vasti and biceps femoris forces, respectively, were observed between the two knee models, which might be attributed to a combination of differing joint contact geometry, ligament behavior, joint kinematics, and muscle moment arms. The tracking strategy developed in this study addressed the inevitable tradeoff between computational cost and model detail in musculoskeletal simulations and can be used with finite element musculoskeletal models to efficiently estimate the interdependence between muscle forces and tissue deformation. |
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| Keywords: | Musculoskeletal modeling Finite element Muscle forces Knee Multiscale |
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