## 4 Discussion
This could be particularly beneficial for populations at risk of injury or re-injury, such as athletes prone to anterior cruciate ligament (ACL) injuries or those recovering from ACL reconstruction. For example, these indices could identify imbalances or deficits in neuromuscular control predisposing individuals to injury. In clinical practice, the indices could be applied to evaluate neuromuscular coordination during rehabilitation, helping clinicians detect persistent abnormalities in muscle coactivation patterns that may indicate compromised knee stability, a known risk factor for ACL injuries. By monitoring the coactivation of key muscle groups like the ST and VL (CoAI2), clinicians can assess the effectiveness of interventions to improve dynamic stability and adjust rehabilitation protocols accordingly. It is worth highlighting that reliability is not assigned to any specific instrument of measurement but rather to the particular testing design and methodology that employ the instrument [33]. Therefore, it should also be emphasized that the testing procedure for the YBT-LQ allows for considerable flexibility in various aspects, which can significantly impact the results [16]. It also has to be highlighted that while this study did not specifically investigate the optimal rest period between stimuli, it is acknowledged that rest duration can influence muscle performance and coactivation patterns, especially during dynamic tasks. Future research could explore the effects of varying rest intervals to determine the most appropriate rest time for minimizing fatigue and ensuring accurate assessments. This would provide a more comprehensive understanding of how rest duration impacts neuromuscular performance in similar contexts. Also, while this study did not account for individual factors such as height, foot size, or limb dominance, these variables could influence the results, particularly in dynamic tasks like the YBT-LQ. Future research should consider these factors. The present study has limitations that must be considered when analyzing the findings. First, the studied sample was restricted to healthy male recreational athletes, so the findings may not apply widely to other groups such as females, older or younger individuals, those with lower limb disorders due to injury or disease, or professional athletes. The available literature shows in the example of other movement tasks that muscle activation patterns differ based on sex and age in healthy individuals [49; 50; 51]. Muscle activation patterns during different movement tasks also differ between healthy individuals and individuals suffering from various diseases and disorders [52; 53; 54]. Secondly, using sEMG is associated with challenges, including signal crosstalk from nearby muscles, variability in electrode placement, and skin impedance, which can impact the accuracy of muscle activation measurements. Only four muscles from the knee joint area were examined in this study. However, knee stability relies on activating a broader set of muscles. Significant contributions to knee coactivation may have been overlooked by not including other important muscles like gastrocnemius. Although this study focuses on knee muscle coactivation during the YBT-LQ, it is important to acknowledge that muscle fatigue can significantly impact coordination and stability, especially during dynamic tasks. Fatigue has been shown to alter coactivation patterns, reducing neuromuscular control and increasing injury risk. However, this study did not directly assess fatigue using EMG measures such as median frequency or zero-crossing rate, which are, as already mentioned, commonly used indicators for detecting muscle fatigue [25]. Future studies could incorporate fatigue analysis to gain more comprehensive insights into how fatigue alters muscle coordination.