Atypical femoral fractures are low-energy fractures initiating in the lateral femoral shaft. We hypothesized that atypical femoral fracture onset is associated with daily femoral strain patterns. We examined femoral shaft strains during daily activities. We analyzed earlier calculations of femoral strain during walking, sitting and rising from a chair, stair ascent, stair descent, stepping up, and squatting based on anatomically consistent musculoskeletal and finite-element models from a single donor and motion recordings from a body-matched volunteer. Femoral strains in the femoral shaft were extracted for the different activities and compared. The dependency between femoral strains in the lateral shaft and kinetic parameters was studied using multi-parametric linear regression analysis. Tensile strain in the lateral femoral shaft varied from 327 με (squatting) to 2004 με (walking). Walking and stair descent imposed tensile loading on the lateral shaft, whereas the other activities mainly imposed tensile loads on the anterior shaft. The multi-parametric linear regression showed a moderately strong correlation between tensile strains in the lateral shaft and the motion kinetic (joint moments and ground reaction force) in the proximal (R(2)=0.60) and the distal shaft (R(2)=0.46). Bone regions subjected to tensile strains are associated with atypical femoral fractures. Walking is the daily activity that induces the highest tensile strain in the lateral femoral shaft. The kinetics of motion explains 46%-50% of the tensile strain variation in the lateral shaft, whereas the unexplained part is likely to be attributed to the way joint moments are decomposed into muscle forces.

Subject-specific musculoskeletal models have become key tools in the clinical decision-making process. However, the sensitivity of the calculated solution to the unavoidable errors committed while deriving the model parameters from the available information is not fully understood. The aim of this study was to calculate the sensitivity of all the kinematics and kinetics variables to the inter-examiner uncertainty in the identification of the lower limb joint models. The study was based on the computer tomography of the entire lower-limb from a single donor and the motion capture from a body-matched volunteer. The hip, the knee and the ankle joint models were defined following the International Society of Biomechanics recommendations. Using a software interface, five expert anatomists identified on the donor's images the necessary bony locations five times with a three-day time interval. A detailed subject-specific musculoskeletal model was taken from an earlier study, and re-formulated to define the joint axes by inputting the necessary bony locations. Gait simulations were run using OpenSim within a Monte Carlo stochastic scheme, where the locations of the bony landmarks were varied randomly according to the estimated distributions. Trends for the joint angles, moments, and the muscle and joint forces did not substantially change after parameter perturbations. The highest variations were as follows: (a) 11° calculated for the hip rotation angle, (b) 1% BW × H calculated for the knee moment and (c) 0.33 BW calculated for the ankle plantarflexor muscles and the ankle joint forces. In conclusion, the identification of the joint axes from clinical images is a robust procedure for human movement modelling and simulation.

Objective: Using a musculoskeletal modelling framework, we aimed to (1) estimate knee joint loading using static optimization (SO); (2) explore different calibration functions in electromyogram (EMG)-informed models used in estimating knee load; and (3) determine, when using an EMG-informed stochastic method, if the measured joint loadings are solutions to the muscle redundancy problem when investigating only the uncertainty in muscle forces. Methods : Musculoskeletal models for three individuals with instrumented knee replacements were generated. Muscle forces were calculated using SO, EMG-informed, and EMG-informed stochastic methods. Measured knee joint loads from the prostheses were compared to the SO and EMG-informed solutions. Root mean square error (RMSE) in joint load estimation was calculated, and the muscle force ranges were compared. Results : The RMSE ranged between 192-674 N, 152-487 N, and 7-108 N for the SO, the calibrated EMG-informed solution, and the best fit stochastic result, respectively. The stochastic method produced solution spaces encompassing the measured joint loading up to 98% of stance. Conclusion: Uncertainty in muscle forces can account for total knee loading and it is recommended that, where possible, EMG measurements should be included to estimate knee joint loading. Significance: This work shows that the inclusion of EMG-informed modelling allows for better estimation of knee joint loading when compared to SO.