## 4 Discussion
Furthermore, while the YBT-LQ was utilized to evaluate dynamic balance and knee coactivation, it is important to note that the specific nature of the test restricts the generalizability of the results to other functional movements, such as running, jumping, or cutting, which are common in athletic settings. Although the intra- and inter-rater reliabilities were considered acceptable, minor differences in electrode placement or participant movement patterns may still introduce some variability in the measurements. Moreover, due to the absence of kinematic data, this study could not directly link muscle activation patterns with joint angles or movement biomechanics during the YBT-LQ, which could limit the understanding of how coactivation contributes to dynamic knee stability. Considering the above limitations, expanding the sample to include more diverse populations in future research is essential. This means introducing studies that include females, older and younger participants than those in the present study, individuals recovering from lower limb injuries, etc., which would improve the generalizability of the findings and provide greater insight into how muscle coactivation may vary across different groups. Also, incorporating more advanced methods, such as high-density sEMG, could help reduce the limitations of crosstalk and improve the spatial resolution of muscle activation data [55]. Additionally, including a broader range of muscles involved in knee joint stability would offer a more comprehensive understanding of coactivation patterns. Future research could test the reliability and validity of the CoAIs across various functional tasks beyond the YBT-LQ, including running, jumping, and agility drills, to extend the applicability of the CoAIs. Longitudinal studies would also be valuable in assessing how knee muscle coactivation changes over time in response to training, fatigue, or rehabilitation. Moreover, integrating kinematic or motion capture data with sEMG analysis would offer deeper insights into the relationship between muscle activation and joint movement, helping to clarify the role of coactivation in maintaining dynamic knee stability during complex, multi-joint movements. These advancements would enhance the clinical applications of the CoAIs, providing a more robust tool for injury prevention and rehabilitation. The described method of calculating the coactivation index by taking the normalized activation level of the antagonist muscle, dividing it by the normalized activation level of the corresponding agonist muscle, and then multiplying by 100 is a common approach and gives a sense of how much the antagonist muscle is activated relative to the agonist during a task. However, this method assumes that both muscles should ideally activate at similar levels, which may not always be the case. Therefore, future studies could be extended by analyzing different approaches that involve comparing the lower activation value to the higher activation value, as this accounts for the relative contribution of both muscles without assuming that one should always dominate. Some methods also involve taking the difference or the sum of the normalized activations to provide a different perspective on coactivation. When analyzing coactivation, it is important to consider muscle function, as agonist and antagonist muscles do not always work in direct opposition; in some cases, one muscle may need to dominate in activation depending on the phase of movement. The ideal coactivation balance can also vary based on the specific demands of the task or movement being performed, such as those related to balance, strength, or stability.