## 4 Discussion  
This single-participant research design study was conducted as a first step toward verifying the hypothesis that RAGT engagement with gradual robotic assistance reduction would promote voluntary muscle contractions, leading to concentrated muscle activity patterns and improved gait quality. Muscle activity patterns were analyzed using EMG while gradually reducing the GF of the Lokomat to ensure that voluntary muscle contractions were maintained. Previous studies have attempted different GF conditions. [PERSON] et al. investigated the GF effects on muscle activation patterns and biomechanical parameters in healthy people [36]. [PERSON] et al. reported that reducing the GF during RAGT in stroke patients with reduced muscle activity increased the activity of certain muscles. They suggested that GF variation should be considered when developing training protocols [41]. [PERSON] et al. [16] investigated the amplitude of muscle activity depending on both GF and BWS, but they tested only 50% and 100% of GF conditions and concluded that GF or BWS generally did not affect the amplitude. There is insufficient evidence to provide detailed recommendations for RAGT, despite previous evidence showing that Lokomat parameters can influence the RAGT effectiveness [42]. Few studies have implemented gradual GF reduction during RAGT, especially in line with the participants' adaptation, as in the present study. As robotic assistance can be a key factor in robotic rehabilitation, the suggested  
Figure 5: Electromyography patterns of right lower limb muscles using the Clustering for Identification of Muscle Activation Patterns algorithm. The blue lines represent the electromyography activation patterns valid for the clustering, and the black lines represent the representative pattern that clustered the blue lines. A blank table indicates that no more than 20 patterns valid for clustering were observed. GF, guidance force. protocol can significantly contribute in itself, given that it allows for an in-depth analysis of neurorehabilitation and motor learning. Another important contribution of this study is that it investigated GF effects in a child with cerebral palsy. Although previous studies have applied RAGT in children with cerebral palsy [12; 16; 17; 43; 44] with promising results regarding robotic rehabilitation, most of them did not focus on gradual GF changes for robotic rehabilitation efficacy enhancement. Children with cerebral palsy undergo a continuous process of growth and development, and the differences in gait ability and growth between children can be large; consequently, it is challenging to recruit suitable candidates for RAGT using the suggested protocols. For children experiencing rapid growth, the kinematic settings of the robot and the user weight input may require significant changes. Nevertheless, children with cerebral palsy were targeted because the concept of \"gradually reducing the robotic guidance while increasing voluntary participation\" was expected to be most effective for children undergoing the development process. When the GF was reduced, the EMG patterns became more distinct. For instance, in the left GMax muscle, the number of muscle activation patterns increased from 28 with one representative pattern at 100% GF to 93 with two representative patterns at 80% GF. Even when the 100% and 95% GF conditions were compared, the EMG deviation decreased. This outcome indicates that full robotic assistance hinders participation, regardless of the participant's willingness to actively participate, compared to when the participant walks with a similar step length by moving the remaining parts on their own, despite reduced robotic assistance. This trend differed in the GF 70% condition. The EMG magnitude increased; however, the deviation also increased. It can be inferred that the large muscle contractions required to independently produce 30% of the force needed for the walking motion were intermixed with inadequate timing.