EMG-Assisted Muscle Force Driven Finite Element Model of the Knee Joint with Fibril-Reinforced Poroelastic Cartilages and Menisci
Esrafilian, A., Stenroth, L., Mononen, M. E., Tanska, P., Avela, J., & Korhonen, R. K. (2020). EMG-Assisted Muscle Force Driven Finite Element Model of the Knee Joint with Fibril-Reinforced Poroelastic Cartilages and Menisci. Scientific Reports, 10, Article 3026. https://doi.org/10.1038/s41598-020-59602-2
Published inScientific Reports
© The Author(s) 2020
Abnormal mechanical loading is essential in the onset and progression of knee osteoarthritis. Combined musculoskeletal (MS) and finite element (FE) modeling is a typical method to estimate load distribution and tissue responses in the knee joint. However, earlier combined models mostly utilize static-optimization based MS models and muscle force driven FE models typically use elastic materials for soft tissues or analyze specific time points of gait. Therefore, here we develop an electromyography-assisted muscle force driven FE model with fibril-reinforced poro(visco)elastic cartilages and menisci to analyze knee joint loading during the stance phase of gait. Moreover, since ligament pre-strains are one of the important uncertainties in joint modeling, we conducted a sensitivity analysis on the pre-strains of anterior and posterior cruciate ligaments (ACL and PCL) as well as medial and lateral collateral ligaments (MCL and LCL). The model produced kinematics and kinetics consistent with previous experimental data. Joint contact forces and contact areas were highly sensitive to ACL and PCL pre-strains, while those changed less cartilage stresses, fibril strains, and fluid pressures. The presented workflow could be used in a wide range of applications related to the aetiology of cartilage degeneration, optimization of rehabilitation exercises, and simulation of knee surgeries. ...
PublisherNature Publishing Group
Publication in research information system
MetadataShow full item record
- Liikuntatieteiden tiedekunta 
Additional information about fundingThis study was financially supported by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 713645, Academy of Finland (grants 286526, 324529) and Sigrid Juselius Foundation. CSC – IT Center for Science Ltd, Finland, is acknowledged for providing FE software.
Showing items with similar title or keywords.
Rahikainen, Ahti (University of Jyväskylä, 2015)
Tsybulko, Vitalii (2019)One problem of current Reinforcement Learning algorithms is finding a balance between exploitation of existing knowledge and exploration for a new experience. Curiosity exploration bonus has been proposed to address this ...
A Data-Driven Surrogate-Assisted Evolutionary Algorithm Applied to a Many-Objective Blast Furnace Optimization Problem Chugh, Tinkle; Chakraborti, Nirupam; Sindhya, Karthik; Jin, Yaochu (Taylor & Francis Inc., 2017)A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives ...
Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm Chugh, Tinkle; Kratky, Tomas; Miettinen, Kaisa; Jin, Yaochu; Makkonen, Pekka (ACM, 2019)We formulate and solve a real-world shape design optimization problem of an air intake ventilation system in a tractor cabin by using a preference-based surrogate-assisted evolutionary multiobjective optimization algorithm. ...
An ultrasound-assisted digestion method for the determination of toxic element concentrations in ash samples by inductively coupled plasma optical emission spectrometry Ilander, Aki; Väisänen, Ari (Elsevier, 2007)method of ultrasound-assisted digestion followed by inductively coupled plasma optical emission spectrometry (ICP-OES) used for the determination of toxic element concentrations (arsenic, barium, cobalt, copper, lead, ...