## 4 Discussion  
This study compared lower-limb muscle activation during isometric rotation using static and dynamic simulations in the AnyBody Modeling System. Activation values were analyzed at the peak moment of force generation. Rather than establishing the superiority of one model over another, the aim was to examine how modeling assumptions influence predicted muscle activity. The results revealed significant discrepancies between muscle activity predictions in static and dynamic simulations. These discrepancies were particularly notable in several hip and thigh muscles, such as the _Gluteus maximus_, _Iliopsos_, and _Sartorius_, whereas muscles like the _Piriformis_ showed more stable activation between models, while _the Tensor fasciae latate_ exhibited some statistically significant differences, suggesting partial sensitivity to simulation type. These differences were further confirmed by statistical analyses, which showed that, for most muscles, the variations in activation were statistically significant (Table 2). Such variability in muscle activation predictions can be attributed to several factors. First, the sensitivity of musculoskeletal systems to modeling inputs and assumptions has been well documented [27; 28; 29]. Even slight variations in segmental mass, joint stiffness, or muscle parameters can produce measurable differences in estimated loads and activations. In the current study, although the same individual anthropometrics and force inputs were applied to both simulations, the mathematical treatment of time-varying inputs and muscle recruitment strategies differed between the two models. This likely contributed to the observed differences in activation patterns. Similar observations have been made in previous validation studies involving musculoskeletal modeling. For instance, [PERSON] et al. compared EMG measurements with muscle activations predicted by the AnyBody Modeling System during walking, hopping, and side-jumping [30]. Their results showed poor agreement between simulated and measured activations (mean kappa < 0.20), particularly in unconstrained tasks such as normal gait. The discrepancies were attributed to model simplifications such as the absence of antagonist co-contraction, the use of idealized joints, and lack of neuromuscular delays. In another study, [PERSON] et al. validated an OpenSim/Moco muscle-driven model against EMG during hopping tasks [31]. They observed that muscle activation predictions were highly muscle-specific; while extensors like the vastus lateralis and soleus matched well with EMG, predictions for flexors and bi-articular muscles such as the rectus femoris were much less accurate. They noted that the optimization objective function (e.g., minimizing squared excitations) and the absence of electromechanical delay in the model could partly explain the timing and amplitude mismatches. The role of task specificity has also been emphasized by [PERSON] et al., who found that generic models calibrated for gait performed poorly in predicting joint loading and muscle activation during high-load tasks such as squats [32]. Their findings support the notion that muscle activity predictions are not universally transferable across tasks and that validation must be task-specific. This idea is echoed in [PERSON] et al., who compared muscle forces predicted using static optimization and forward dynamics during a single-leg hop [33]. While major extensors yielded relatively consistent results, bi-articular muscles like the gastrocnemius and hamstrings showed large discrepancies in activation profiles, highlighting how methodological choices can influence output. Additional insights into model-to-model variability were offered by [PERSON] et al., who compared six different musculoskeletal models using the same pediatric gait dataset [34]. Although general trends in joint kinematics and kinetics were preserved across models, predicted muscle activity varied substantially. Four of six models captured EMG-likeactivation in the rectus femoris, while others did not.