# Perspectives and Challenges in Robotic Neurorehabilitation
## 2 An Overview of Robotic Devices for Neurorehabilitation
### Robotic Neurorehabilitation for the Upper Limb
The low-level implementation of these assistive training modalities is more challenging in exoskeletons than in end-effector devices. In particular, with the goal to exploit exoskeletons to also improve inter-joint coordination in the neurological population [72], several innovative control schemes have been designed. Starting from the common control schemes implemented in end-effector devices and exoskeletons, there is closed-loop feedback control with feedforward components. This scheme permits the correction of patients' performance errors and compensates for the weight, inertia, and friction of the device mechanisms. To obtain the error signal of the feedback loop, the ability to sense some kinematic variable (positions, velocities) or interaction forces and then compare them with a pre-determined reference trajectory is needed [58]. Instead, the feedforward components can be computed by the robot-model or can be learnt with iterative techniques [52]. This control strategy is more commonly implemented in exoskeletons using position information to close the loop [73; 74]. Assistive strategies are also implemented by exploiting the interaction control framework [75]. In particular, most of the end-effector devices adopt impedance control schemes while exoskeletons adopt admittance control schemes. In the first case, the controllers use position feedback in order to regulate the mechanical impedance of the robot (a non-linear generalization of the mechanical stiffness). The second case is the opposite: The controllers use force feedback in order to regulate the position of the specific joints (based on a model of the system). Exoskeletons usually implement admittance control schemes due to the usual lack of back-drivability of their mechanisms. In recent years, more complex schemes have been developed for upper limb exoskeletons: Sliding mode controllers [76] or controllers triggered by the intention detection of the patient computed by electrophysiological measurements (i.e., surface electromyography (sEMG) [77; 78] and electroencephalography (EEG) [79]). All the aforementioned methods are based on the comparison of an error signal with a reference trajectory. With the end-effector devices, this trajectory can be easily computed or designed while with exoskeletons there are many issues to be solved. As already said, exoskeletons are potentially used to restore patient inter-joint coordination by properly tuning the different robot joint trajectories; however, the relation between recovery and exoskeleton trajectories is still unclear. The design of appropriate reference trajectories for each joint is a real challenge. Some methods have recently been proposed: (1) Reproduction of previously recorded trajectories performed by healthy subjects [80]; and (2) use of previously recorded pathological involuntary joint torques translated in the joint kinematics domain [81]. In order to have a more complete idea of a robot for upper limb neurorehabilitation, in the following paragraph, we briefly introduce a new robot developed in our laboratories, as a case study. End-Effector Device for Wrist Rehabilitation: WristBot  
WristBot is an end-effector robotic device designed for the wrist neurorehabilitation of patients with neurological or orthopedic disabilities (Figure 1). It was developed in the Motor Learning, Assistive and Rehabilitation Robotics laboratory of the Italian Institute of Technology (IIT). The robot allows movements along the three wrist articulations, with a range of motion similar to a typical human subject: \(\pm 62^{\circ}\) in flexion/extension, \(45^{\circ}/40^{\circ}\) in radial/ulnar deviation, and \(\pm 60^{\circ}\) for pronation/supination movements [82]. It is provided with four brushless motors that allow guidance and assistance of wrist movements in the three above-mentioned planes, with a maximum torque of 1.53 Nm in flexion/extension, 1.63 Nm in radial/ulnar deviation, and 2.77 Nm in pronation/supination movements.