# Analysis of Surface EMG Parameters in the Overhead Deep Squat Performance
## 4 Discussion  
The fundamental question we are looking to answer in this study is whether, based on the sEMG signal recorded during physical exercise and our proposed parameters (%MVC, \(MDF\), \(MF\), kurtosis, and \(CAI\)), we can assess the correctness of a deep squat performance. It is an important issue because if the squat is executed correctly, it can be not only a safe and effective exercise in the rehabilitation process but also a good development exercise for athletes [10]. Our research can provide useful parameters that could support the development of an objective method for assessing the quality of performing a deep squat. All indicated parameters were analyzed, but the most interesting results were obtained for %MVC, \(MDF\), and \(CAI\), so in the Discussion we focus on these three parameters. The statistical analysis for the lower and upper body muscles (Tables 7 and 8) indicated statistically significant differences with at least moderate and in some cases even large effect sizes in muscle excitation and median frequency in individual groups 1-3, depending on the technical quality of the deep squat performance. The results of the statistical analysis for %MVC and \(MDF\) are promising and confirm that our thesis is valid for a small sample size, but it still needs further justification. In the first group, where physiotherapists evaluated the technical quality of the deep squat as the worst, there were significant differences in muscle excitation expressed in %MVC between RF and BF (\(p\) = 0.0193) and between VM and BF (\(p\) = 0.0333). The \(MDF\) parameter confirms the statistically significant difference between RF and BF (\(p\) = 0.0039) but also indicates statistically significant differences for RF and VM (\(p\) = 0.0117) and for RF and GM (\(p\) = 0.0011). It can suggest that the quadriceps muscle (RF, VM) takes over the work, which may indicate an incorrect movement pattern (e.g., too much forward knee). According to [PERSON] et al. [19], a poorly performed squat may change lower limb alignment, increasing the knee valgus, which may expose the lower limb joints too much torque, which may require adaptive muscle activation strategies to stabilize the lower limbs joints. In this group, the average \(CAI\) values (Table 9) are the lowest, which can indicate problems with knee and shoulder joint control. Similar findings concerning co-activation were reported by [PERSON] et al. [39]. They concluded that co-activation of the antagonist is necessary to aid the ligaments in maintaining joint stability. In the second group, in which the correctness of performing the deep squat increased, statistically significant differences in excitation were also observed between RF and BF (\(p\) = 0.0019) and between VM and BF (\(p\) = 0.0000), as they were for group 1, but additionally, a statistically significant difference appears in the excitation of RF and GM (\(p\) = 0.0000) and of VM and GM (\(p\) = 0.0000). We can observe greater involvement of quadriceps muscles (VM, RF) and moderate activity of the gluteus and biceps femoris; there is no dominance of one segment. In this group, \(MDF\) also confirmed the statistically significant differences obtained for the first group (RF and BF, RF and VM, RF and GM), but in addition, a significant difference between VM and GM was detected. Muscle co-activation indexes (Table 9) in this group were slightly higher than those in group 1, suggesting improved coordination. The research of [PERSON] et al. [40] showed that co-activation of agonist and antagonist muscles (e.g., quadriceps and biceps femoris) is important for knee joint stabilization, which may translate into improved technique in exercises such as the overhead squat but still does not guarantee full control and stability in demanding movements. In the third group of subjects, who performed the deep squat the best, there was a statistically significant difference only between the excitation of RF and GM (\(p\) = 0.0011) and of VM and GM (\(p\) = 0.0002).