# Modeling of Muscle Activation from Electromyography Recordings in Patients with Cerebral Palsy
## 4 Discussion  
In the current work, EMG signals collected from lower extremity muscles whilst participants were cycling on an ergometer, were converted into muscle activation profiles. In the study by [PERSON] et al. [20; 21], EMG signals were normalized by dividing the recorded signal by the value of maximum voluntary contraction. This is a common scaling method for EMG signal in sports and performance diagnostics, used to avoid the equivalent scaling of the low electrical activity and high electrical activity of the muscles. In patients with CP, however, and, especially those with spasticity, it is not possible to record voluntary muscle contractions. For patients with spasticity, the central activation of the muscle is disturbed and, as a result, they do not have voluntary access to their own muscular system. On the other hand, the reflex activation of the muscles, which is fully preserved as it is initiated by peripheral sensors and is involuntary, likely mixes with maximum muscle contractions. The plots obtained after normalizing the EMG signal with the maximum value of the individual muscle's EMG signal were similar to those described previously--for example, by [20]. No differences in muscle activation profiles were observed between the group of patients with CP and the group of healthy controls. This is due to the fact that parameters \(\alpha\), \(\beta_{1}\) and \(\beta_{2}\), which map the correspondence between the EMG signal \(E(t)\) and the intermediate signal \(n(t)\), as well as parameter \(A\) mapping the conversion \(n(t)\to a(t)\), were given the same values for both groups. This was done as no information is available in the literature specifically for patients with CP, and assuming different values would have been equally speculative. Furthermore, it is unlikely that muscular activation is different in CP. Given the origin of impairments in CP, alterations probably occur at a previous stage, at the level of the neuronal input to motor neurons and from these to muscle fibers. Comparison of muscle force patterns, derived from kinematic variables during the experiment, with the equivalent patterns derived from \(a(t)\) will enable using the future parameter values for the group with CP that are more realistic. The tendency of increasing ND values of muscle activation with the level of spasticity MAS (Figure 6) indicates larger deviations of muscle activation from reference values with increasing  
Figure 6: Variation of Norm Distance (ND) with respect to MAS level for four muscles activity converted from EMG signal. (**a**) for M. rectus femoris, (**b**) for M. biceps femoris, (**c**) for M. tibialis anterior and (**d**) for M. gastrocnemius. The number of data points changes for different panels because EMG signals collected from some muscles had poor quality and had to be discarded from analysis. spasticity. The fact that this tendency is observed for all four muscles analyzed here indicates that the deviations observed are characteristic of spasticity. Hence, they can potentially provide additional objective information, based on EMG recordings, on the level of spasticity of a patient. A larger sample of data points would be nevertheless necessary to confirm this. In addition to this, assuming that deviations in muscle activation with spasticity occur at any point along the cycle path, it is likely that muscle force also deviates from muscle force patterns in healthy participants. A result of this would be a less regular speed of cycling in patients with a higher MAS level. Some additional limitations of the study need to be mentioned. First, displacement of electrodes during cycling may have resulted in crosstalk. This may affect the shape of the recorded EMG signal and lead to the estimation of unrealistic muscle activation patterns. Variability in the measured signal may have also contributed to the latter. Finally, the importance of the mapping parameters [20; 30] for the conversion of \(n(t)\to a(t)\) show the need for ways to find optimal values for these parameters.