## 4 Discussion
EMG amplitude also requires normalization to subject-specific maxima, which introduces additional variability and potential bias in between-subject comparisons [39]. Previous studies have shown limited agreement between EMG recordings and muscle activations predicted by musculoskeletal models, especially during complex or unconstrained tasks. For example, [PERSON] et al. reported poor correspondence between measured and simulated activity during walking and jumping [28], while [PERSON] et al. observed significant discrepancies between modeled and recorded activation in a hopping task [29]. Given these challenges, and considering that the primary objective of this study was to compare static and dynamic simulation approaches--not to validate model outputs against EMG--we concluded that musculoskeletal modeling alone provides a sufficient framework for comparative analysis. This is particularly relevant for muscles whose activation patterns cannot be measured noninvasively, such as the deep hip rotators that play a central role in isometric external rotation. Future work could incorporate EMG data to validate simulation outcomes for superficial muscles. For instance, [PERSON] et al. recently demonstrated that a rigorous EMG acquisition and preprocessing protocol (utilizing a 16-channel wireless system at 1000 Hz, with band-pass filtering between 10-400 Hz, full-wave rectification, low-pass filtering \(\sim\)6 Hz, and normalization to maximal voluntary contractions) can enable the direct comparison of measured muscle activity with model-predicted activations [40]. Notably, their musculoskeletal model's output closely matched the normalized EMG activation levels across multiple lower-limb muscles, underscoring the potential of EMG-based validation for confirming model reliability. Adopting a similar approach in a prospective study could thus strengthen confidence in the simulation outcomes for accessible muscle groups. Additionally, testing other modeling environments or control strategies, such as those driven by motion capture systems, may enhance the understanding of how methodological choices influence simulation outcomes. The integration of more detailed muscle-tendon models, such as Hill-type or three-element models, could also improve physiological accuracy, particularly in tasks involving complex joint coordination or rapid loading. Additionally testing other modeling environments or control strategies, such as those driven by motion capture systems, may enhance the understanding of how methodological choices influence simulation outcomes. Another important consideration is the exclusion of ankle muscles, such as the per-oneus longus and peroneus brevis, which can contribute to outward foot rotation. These muscles were not included in the musculoskeletal model, as their primary functions are foot eversion and ankle stabilization rather than generating hip rotation [41]. Moreover, with the feet securely fixed in our isometric setup, their ability to influence axial limb rotation is minimal [42]. Therefore, the analysis focused on the major hip external rotators and knee-stabilizing muscles described above. It should be noted that the model's predictions depend on certain underlying assumptions, particularly the anthropometric scaling approach and the muscle force constraints used. In this study, we applied a standard linear scaling based on each subject's height and mass to personalize the model, a method that is widely adopted for musculoskeletal simulations. While this ensures subject-specific segment geometry and strength, different scaling techniques or calibrations could alter these parameters and thereby influence the muscle activation outcomes [27]. Similarly, muscle recruitment in our simulations was determined by an optimization criterion (minimizing the sum of cubed muscle forces normalized by their maxima) under the constraint that no muscle exceeds its maximum force capacity.