## 3 Results  
The robot used in the experimental tests was equipped with a Nema 34 stepper motor and a 1000 CPR (count per revolution) encoder. The actuator technical specifications are given in Table 1.  
To measure the current, the ACS712 module based on the hall effect principle has been used. This module can measure both AC and DC current providing a perfect isolation from the load and easy integration with any conventional micro-controller. On the other hand, a Myoware muscle sensor was used to measure a filtered and rectified electrical activity of the muscle with a resolution of 12 bits. The EMG signal was acquired using the multi-channel surface electromyography (SEMG) signal during \begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Bipolar Stepper** \\ \hline Step Angle & 1.8 deg \\ \hline Holding Torque & 4.6 N.m \\ \hline Rated Current & 6 A \\ \hline Encoder Driving Voltage & 5 V \\ \hline Encoder Output Current & 55 mA \\ \hline Encoder Resolution & 1000 CPR \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Technical specifications of the robot actuator. Figure 4: Operating system flowchart. dynamic contraction. Myoelectric signals were detected by placing three electrodes--two of them for measurement with a distance equal to 3 cm, and the third serving as a reference electrode placed at the proximal end of the elbow. The selection of muscles and the placement of the pre-gelled electrodes are based on the related literature [51] for elbow flexion/extension. Based on [52], the maximum bandwidth of the EMG signal is approximately 500 Hz. Therefore, according to Shannon theory, the sample rate should be greater than twice the signal bandwidth. Thus, the sampling frequency of the acquisition system was fixed at 1000 Hz. The patient should receive specific training to learn how the rehabilitation system is used, including the placement of the electrodes and the installation of the system. The main features of the EMG sensor are given in Table 2.  
The developed user-friendly HMI containing three different control screens. The first screen shown in Figure 5 has the role of configuration and supervision. In this step, the interface provides an authentication service. This service improves the security aspect of the networked system. Since we are in the context of vision-based control using a 3D camera, the position of the user must respect specific parameters to be correctly recognized. To solve this problem, the developed software provides some indicators on the user 3D position. When the physiotherapist's position in front of the 3D camera is inappropriate for control, the indicators are displayed in red, as shown in the first screen of the Figure 5. In order to get the actual information, the Kinect camera requires a predefined position. If the user shows up outside this position, the system interface displays an invalid position alert (see Figure 5). When the appropriate position is detected, the three indicators of the user's position become colored green, as shown in Figure 5.  
Figure 6 shows that the system offers real time streaming. An integrated IP camera in the developed control architecture is used to send videos to the router. The physiotherapist's computer connected to the router displays a real time video of the patient during the rehabilitation process. Such visual interaction could facilitate the process of rehabilitation. By controlling the robot in a gestural way, the video of the patient following the robot's actions is not enough for the physiotherapist. The physiotherapist must know the real time angle achieved by the elbow in motion.