ArticleJournal of biomechanical engineering2016
Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis.
Article in Journal of biomechanical engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Subject-specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models.Journal of anatomy · 2020Trial
- Dependence of Muscle Moment Arms on In Vivo Three-Dimensional Kinematics of the Knee.Annals of biomedical engineering · 2017Trial
- Stretch versus shortening contractions subsequently decrease versus increase neural drive to the human tibialis anterior.Experimental physiology · 2026Article
- A PRISMA systematic review through time on predictive musculoskeletal simulations.Journal of neuroengineering and rehabilitation · 2025Article
- The impact of PCA derived gait kinematic variations on estimated medial knee contact forces in a knee osteoarthritis population.Scientific reports · 2025Article
- Muscle-driven simulations and experimental data of cycling.Scientific reports · 2023Article
- Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific musculoskeletal anatomy.PLoS computational biology · 2023Article
- Reproducibility in modeling and simulation of the knee: Academic, industry, and regulatory perspectives.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2023Review
- Uncertainty in Muscle-Tendon Parameters can Greatly Influence the Accuracy of Knee Contact Force Estimates of Musculoskeletal Models.Frontiers in bioengineering and biotechnology · 2022Article
- Predictions of Anterior Cruciate Ligament Dynamics From Subject-Specific Musculoskeletal Models and Dynamic Biplane Radiography.Journal of biomechanical engineering · 2021Article
- Subject-Specific Spino-Pelvic Models Reliably Measure Spinal Kinematics During Seated Forward Bending in Adult Spinal Deformity.Frontiers in bioengineering and biotechnology · 2021Article
- Knee abduction moment is predicted by lower gluteus medius force and larger vertical and lateral ground reaction forces during drop vertical jump in female athletes.Journal of biomechanics · 2020Article
- The association between periacetabular osteotomy reorientation and hip joint reaction forces in two subgroups of acetabular dysplasia.Journal of biomechanics · 2020Article
- Linking Joint Impairment and Gait Biomechanics in Patients with Juvenile Idiopathic Arthritis.Annals of biomedical engineering · 2019Article
- A Critical Analysis of TKR In Vitro Wear Tests Considering Predicted Knee Joint Loads.Materials (Basel, Switzerland) · 2019Article
- A computationally efficient strategy to estimate muscle forces in a finite element musculoskeletal model of the lower limb.Journal of biomechanics · 2019Article
- A lower extremity model for muscle-driven simulation of activity using explicit finite element modeling.Journal of biomechanics · 2019Article
- In vivo human lower limb muscle architecture dataset obtained using diffusion tensor imaging.PloS one · 2019Article
- Loading and kinematic profiles for patellofemoral durability testing.Journal of the mechanical behavior of biomedical materials · 2018Article
- The interaction of muscle moment arm, knee laxity, and torque in a multi-scale musculoskeletal model of the lower limb.Journal of biomechanics · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Musculoskeletal models are powerful tools that allow biomechanical investigations and predictions of muscle forces not accessible with experiments. A core challenge modelers must confront is validation. Measurements of muscle activity and joint loading are used for qualitative and indirect validation of muscle force predictions. Subject-specific models have reached high levels of complexity and can predict contact loads with surprising accuracy. However, every deterministic musculoskeletal model contains an intrinsic uncertainty due to the high number of parameters not identifiable in vivo. The objective of this work is to test the impact of intrinsic uncertainty in a scaled-generic model on estimates of muscle and joint loads. Uncertainties in marker placement, limb coronal alignment, body segment parameters, Hill-type muscle parameters, and muscle geometry were modeled with a global probabilistic approach (multiple uncertainties included in a single analysis). 5-95% confidence bounds and input/output sensitivities of predicted knee compressive loads and varus/valgus contact moments were estimated for a gait activity of three subjects with telemetric knee implants from the "Grand Challenge Competition." Compressive load predicted for the three subjects showed confidence bounds of 333 ± 248 N, 408 ± 333 N, and 379 ± 244 N when all the sources of uncertainty were included. The measured loads lay inside the predicted 5-95% confidence bounds for 77%, 83%, and 76% of the stance phase. Muscle maximum isometric force, muscle geometry, and marker placement uncertainty most impacted the joint load results. This study demonstrated that identification of these parameters is crucial when subject-specific models are developed.
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