Musculoskeletal lower limb models have been shown to be able to predict hip contact forces (HCFs) that are comparable to in vivo measurements obtained from instrumented prostheses. However, the muscle recruitment predicted by these models does not necessarily compare well to measured electromyographic (EMG) signals. In order to verify if it is possible to accurately estimate HCFs from muscle force patterns consistent with EMG measurements, a lower limb model based on a published anatomical dataset (Klein Horsman et al. 2007. Clin Biomech, 22. 239-247) has been implemented in the open source software OpenSim. A cycle-to-cycle hip joint validation was conducted against HCFs recorded during gait and stair climbing trials of four arthroplasty patients (Bergmann et al. 2001. J Biomech, 34, 859-871). Hip joint muscle tensions were estimated by minimizing a polynomial function of the muscle forces. The resulting muscle activation patterns obtained by assessing multiple powers of the objective function were compared against EMG profiles from the literature. Calculated HCFs denoted a tendency to monotonically increase their magnitude when raising the power of the objective function; the best estimation obtained from muscle forces consistent with experimental EMG profiles was found when a quadratic objective function was minimized (average overestimation at experimental peak frame: 10.1% for walking, 7.8% for stair climbing). The lower limb model can produce appropriate balanced sets of muscle forces and joint contact forces that can be used in a range of applications requiring accurate quantification of both.

The validation of musculoskeletal models is a challenging task necessary to obtain confidence in the numerical predictions they can provide. In this paper, a musculoskeletal model of the lower limb is used to predict the hip contact forces and muscle activations resulting from walking at different speeds for three total hip replacement patients implanted with instrumented prostheses (Bergmann et al., J. Biomech. 34:859–871, 2001). The developed model is shown to estimate the magnitude of hip contact forces with encouraging accuracy in terms of relative peak error (on average within 22% of the experimental value) and global prediction error measurements. Hip contact force predictions were found to be generally more accurate for a slow walking speed. The static optimization technique adopted to estimate muscle activation profiles reproduced for the majority of muscles the modulation and variation in activation patterns documented in the literature for different walking speeds.

In the literature, lower limb musculoskeletal models validated against in vivo measured hip contact forces (HCFs) exhibit a tendency to overestimate the HCFs magnitude and predict inaccurate components of the HCF vector in the transverse plane. In order to investigate this issue, a musculoskeletal model was forced to produce HCFs identical to those measured and the resulting joint equilibrium equations were studied through both a general approach and a static optimization framework. In the former case, the existence of solutions to the equilibrium equations was investigated and the effect of varying the intersegmental moments and the muscle tetanic stress assessed: for a value of 100 N/cm2 and moments calculated from an inverse dynamics analysis on average only 62% of analyzed frames were solvable for level walking and 70% for stair climbing. In the static optimization study, the model could reproduce the experimental HCFs but the recruited muscles were unable to simultaneously equilibrate the hip intersegmental moments without the contribution of reserve moment actuators. Without constraints imposed on the HCFs, the predicted HCF vectors presented maximum angle deviations up to 221 for level walking and 331 for stair climbing during the gait stance phase. The influence of the medio-lateral HCF component on the solvability of the equilibrium equations and the muscle recruitment alteration when the model was forced to produce the experimental HCFs suggest that a more accurate geometrical representation of the gluteal muscles is mandatory to improve predictions of the HCF vector yielded by the static optimization technique.