Robotic gait training helps the nervous system recover and strengthen weak muscle groups. Many studies in the literature show that applying robotic gait rehabilitation to patients with neurological disorders such as Multiple Sclerosis (MS), Stroke and Spinal Cord Injection (SCI) effectively restores gait ability. In contrast to the studies in the literature that included only healthy individuals, both the control and patient groups were formed and detailed analyses were carried out for both groups. In this study, EMG signals in GMA, GME, ILP, BF, VM, MG, TA muscles were recorded simultaneously with a different electrode placement during robotic gait for the first time in literature and then a location that prevents a phase shift was presented. The classification performance has also been increased by removing 26 different attribute parameters like time, frequency and statistics from the signals instead of gait studies with a maximum of 12-16 traits extraction. The extracted features were classified with the approaches Multilayer Perceptron Neural Networks (MLP), Support Vector Machines (SVM), K-Nearest Neighbourhood algorithm (KNN), Random Forest Classification Algorithm (RF) and Deep Learning and then a detailed performance comparison have been realized. Among the approaches compared the Stochastic Gradient Optimization Algorithm-based deep learning structure produced the best performance with 98.5714% accuracy. It was also seen that it is essential to plan the exoskeleton and the robotic gait pattern suitable for patients' disease state and muscle activation.