The goal of this project is to critically evaluate the ability of musculoskeletal models to predict muscle and joint contact forces in the knee reliably during walking. Knowledge of these internal loads could improve the diagnosis and treatment of neuromusculoskeletal disorders that affect walking ability (e.g., stroke, cerebral palsy, osteoarthritis). Because internal loads cannot be measured clinically, musculoskeletal models have become the primary means for developing estimates. However, if model estimates are inaccurate, clinical assessments or treatments based on these estimates could be ineffective or even harmful. We propose to evaluate musculoskeletal model estimates of muscle and joint contact forces in the knee during walking using in vivo contact force measurements obtained from patients implanted with force-measuring knee replacements. These unique internal load measurements will allow us to evaluate contact force estimates directly and muscle force estimates indirectly. For each of the five patients tested, we will collect a broad range of movement data (tibial contact force, motion capture, ground reaction force, EMG, fluoroscopic, muscle strength). We will then enhance OpenSim open-source musculoskeletal modeling software with new capabilities (e.g., """"""""fast"""""""" contact model modeling methods, new optimization methods for predicting muscle forces based on EMG measurements) to permit construction of a high-fidelity musculoskeletal model of each patient. The ability of each patient-specific model to reproduce the patient's tibial contact force, EMG, and other movement data will be evaluated using existing and new muscle and contact force prediction methods. We will also hold an annual competition at the ASME Summer Bioengineering Conference where researchers will use data and models we make available to predict the in vivo tibial contact forces without knowing them in advance. This musculoskeletal model validation effort will be the most extensive ever performed, and the data, models, and ideas generated will provide a foundation for further evaluation studies for years to come.

Public Health Relevance

Musculoskeletal models could facilitate the design of effective, customized treatments for neuromusculoskeletal disorders such as stroke, cerebral palsy, and osteoarthritis. However, before they can be used for this purpose, their predictions need to be validated. This study proposes unique data and methods to perform such a validation with a focus on the knee during walking.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
Project #
Application #
Study Section
Musculoskeletal Rehabilitation Sciences Study Section (MRS)
Program Officer
Peng, Grace
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Florida
Engineering (All Types)
Schools of Engineering
United States
Zip Code
Eskinazi, Ilan; Fregly, Benjamin J (2018) A computational framework for simultaneous estimation of muscle and joint contact forces and body motion using optimization and surrogate modeling. Med Eng Phys 54:56-64
Pizzolato, C; Reggiani, M; Modenese, L et al. (2017) Real-time inverse kinematics and inverse dynamics for lower limb applications using OpenSim. Comput Methods Biomech Biomed Engin 20:436-445
Pizzolato, Claudio; Reggiani, Monica; Saxby, David J et al. (2017) Biofeedback for Gait Retraining Based on Real-Time Estimation of Tibiofemoral Joint Contact Forces. IEEE Trans Neural Syst Rehabil Eng 25:1612-1621
SerrancolĂ­, Gil; Kinney, Allison L; Fregly, Benjamin J et al. (2016) Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking. J Biomech Eng 138:
Eskinazi, Ilan; Fregly, Benjamin J (2016) An Open-Source Toolbox for Surrogate Modeling of Joint Contact Mechanics. IEEE Trans Biomed Eng 63:269-77
Eskinazi, Ilan; Fregly, Benjamin J (2015) Surrogate modeling of deformable joint contact using artificial neural networks. Med Eng Phys 37:885-91
Mizu-Uchi, Hideki; Colwell Jr, Clifford W; Flores-Hernandez, Cesar et al. (2015) Patient-specific computer model of dynamic squatting after total knee arthroplasty. J Arthroplasty 30:870-4
Pizzolato, Claudio; Lloyd, David G; Sartori, Massimo et al. (2015) CEINMS: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks. J Biomech 48:3929-36
Walter, Jonathan P; Korkmaz, Nuray; Fregly, Benjamin J et al. (2015) Contribution of tibiofemoral joint contact to net loads at the knee in gait. J Orthop Res 33:1054-60
Walter, Jonathan P; Kinney, Allison L; Banks, Scott A et al. (2014) Muscle synergies may improve optimization prediction of knee contact forces during walking. J Biomech Eng 136:021031

Showing the most recent 10 out of 23 publications