###### Abstract  
The last few years have seen significant advances in neuromotor rehabilitation technologies, such as robotics and virtual reality. Rehabilitation robotics primarily focuses on devices, control strategies, scenarios and protocols aimed at recovering sensory, motor and cognitive impairments often experienced by stroke victims. Remote rehabilitation can be adopted to relieve stress in healthcare facilities by limiting the movement of patients to clinics, mainly in the current COVID-19 pandemic. In this context, we have developed a remote controlled intelligent robot for elbow rehabilitation. The proposed system offers real-time monitoring and ultimately provides an electronic health record (EHR). Rehabilitation is an area of medical practice that treats patients with pain. However, this pain can prevent a person from positively interacting with therapy. To cope with this matter, the proposed solution incorporates a cascading fuzzy decision system to estimate patient pain. Indeed, as a safety measure, when the pain exceeds a certain threshold, the robot must stop the action even if the desired angle has not yet been reached. A fusion of sensors incorporating an electromyography (EMG) signal, feedback from the current sensor and feedback from the position encoder provides the fuzzy controller with the data needed to estimate pain. This measured pain is fed back into the control loop and processed to generate safe robot actions. The main contribution was to integrate vision-based gesture control, a cascade fuzzy logic-based decision system and IoT (Internet of Things) to help therapists remotely take care of patients efficiently and reliably. Tests carried out on three different subjects showed encouraging results. rehabilitation robotics; human-robot interaction; gesture control; Internet of Things +
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Much attention has been given to the development of robotics for teleoperation purposes [8; 9; 10]. Several human-robot gesture recognition interfaces have been developed [11; 12]. The Kinect camera shows excellent vision capabilities. Thus, it has been used in many applications, such as people assistive systems [13; 14; 15], rehabilitation and stimulation [16; 17] and game-based learning systems [18]. The Kinect device is able to recognize human movement. The recognized human patterns are used in several computer environments, such as LabVIEW [11] and MATLAB [19]. In [20], a Kinect gesture recognition approach was proposed to improve the gesture implementation process. Additionally, an eigenspace-based approach was introduced in [21] to identify and recognize human gestures using 3D Kinect. A Kinect-based tracking approach was presented in [22]. This approach is mainly for pick and place applications. Thus, the Kinect sensor offers excellent capabilities to be used for remote handling purposes. In addition to the vision-based solutions, electromyography (EMG) has been widely used in robotic applications [14; 23; 24; 25; 26; 27]. A real time telemanipulation technique is presented in [23] using EMG-based arm tracking and markless images. Based on EMG signals a myoelectrically controlled robotic system was proposed for elbow training assistance [14]. In [24], EMG was used to control in real time a robotic arm system. In [25], the authors designed a rehabilitation training system based on virtual reality and EMG feedback. A novel concept of robot therapy using EMG thresholds was presented in [26]. A review on the EMG-based control of the human upper limb motion is provided in [27]. Additionally, Fuzzy control has been extensively used in many robotic applications [28; 29; 30] because of its power in modeling uncertainty and vague data. Many researches have been focused on fuzzy logic-based control schemes for rehabilitation robots. An adaptive admittance controller based on fuzzy logic has been developed for 2-DOF actuated parallel ankle rehabilitation robot [31]. A PID controller based on a fuzzy inference system for rehabilitation of shoulder flexion/extension has been presented in [32].