## 5 Discussion  
In this study, the authors introduced a methodology tailored for rapid control prototyping of control strategies in the field of rehabilitative robotics. Utilizing MATLAB-Simulink, the control architecture was exported and executed in real-time on the Speedgoat target machine: high-level code (Simulink) was automatically transformed into C/C++ (build duration: 0 h 5 m 15.516 s) code on a real-time target machine. Various control strategies, with a focus on those incorporating compliant control techniques such as transparent and impedance control, were tested to provide adaptable assistance to users. The authors presented the hardware platform used to test the 6-DoFs upper limb exoskeleton (Figures 1 and 2). The geometric model of the exoskeleton, obtained from CAD software, was utilized to design a rigid body tree model (Figure 3a) encompassing both kinematic and dynamic properties. Successively, the control model implemented in Simulink (Figure 5), explaining its state machine logic and various functionalities, is presented. Additionally, they showcased the graphical interface designed to facilitate device usage and testing (Figure 6). Performance evaluation test (Figure 3b) for SEA sensor readings and the dynamic model designed for gravity torque compensation yielded comparable results (see Table 1). The observed errors can be attributed to inertial and viscous effects not accounted for in the proposed dynamic model, which relies on MATLAB's Robotic Toolbox's built-in function. The proposed model (as depicted in Figure 4) only performs gravity compensation based on positions, neglecting inertial effects due to acceleration. However, within the context of rehabilitation, this compromise is acceptable given the typical low velocities and accelerations involved. Since the inertial properties of the system can be obtained from CAD, a future step could involve utilizing the rigid-body-tree to generate the mass matrix and also performing inertial component compensation. Assistive control testing (Figure 8), utilizing impedance in joint space (Figure 7), was instrumental in selecting and testing various combinations of stiffness and viscosity. The objective was to achieve the tuning of modular assistance levels tailored to rehabilitation requirements. The presented tests were conducted to evaluate the performance of the device, particularly to establish the exoskeleton's ability to provide different levels of assistance to the user, thanks to the implementation of the proposed impedance control strategy. Experiments were conducted with three levels of assistance, adjusting stiffness-viscosity parameters of impedance control (values listed in Table 2). Figure 8 illustrates the results, with the black line representing the target trajectory for the joint, while the blue, orange, and yellow lines depict outcomes of tests conducted at different assistance levels. The observed oscillations result from human interaction with the robot. By assigning a different spring-damper behavior, variable assistance could be selected. Lower assistance (high compliance and low stiffness) permits increased interaction with the robot, allowing users to deviate significantly from the target trajectory. Conversely, higher assistance (low compliance and high stiffness) ensures better adherence to the target trajectory, albeit with limited interaction with the device. In this context, RCP facilitated various interactions to experimentally evaluate suitable pairs of \(k_{v}\) and \(\beta_{v}\) by dynamically adjusting parameters in real-time and observing the device's behavior. While the proposed method offers simplicity and abstraction from low-level issues, facilitating rapid testing of control models, it has limitations. Specifically, its inability to access the underlying algorithms of the adopted functions and toolboxes.