## 1 Introduction
Because all these factors are not easily controllable in many aspects, it is necessary to develop a rehabilitation robot that is affordable, clinically feasible, and portable, which will make the robot more accessible, and eventually maximize the task-specific repetition of the paralyzed limb. For the robots to be applicable in daily activities, they need to be portable, simple, controllable according to the user's intent, and be able to involve real target objects instead of providing a monitor screen or virtual reality. Most of the robots use force sensors, torque sensors, or surface electromyography to recognize the user's intent [23]. However, it is difficult for people with severe limb impairment to generate sufficient input signals for such sensors. Recently, active research on brain-machine interface (BMI) is being conducted to extract user-intent directly from the brain signals. However, it is very challenging to perform precise control of the robot with electroencephalography signals, which is the most commonly used non-invasive brain signal, because the signal-to-noise ratio is very low. Furthermore, BMI technology involving invasive brain signals, such as intracortical signals or electrocorticography (ECoG), is far from practical utilization [24; 25; 26]. To maximize neuroplasticity in using rehabilitation robots, the robot should move according to the user's intent, or at least the patient should be able to anticipate the robot's movement. Electromyography (EMG) and torque sensor-based controls are not applicable to patients with flacied paralysis or with only minimal volitional movements. In this study, we attempted to apply an image processing-based approach that can reflect user-intent. Visual compensation using camera images in the BMI control of the robotic arm hasrecently been introduced [27]. [PERSON] et al. [28] suggested an upper limb rehabilitation robot system for precision control by camera-based image processing. We hypothesized that visual compensation using a camera image-processing algorithm could assist the exoskeleton robot to be actuated according to the user-intent, and therefore complete the eye-brain-limb-object neural feedback loop, which may facilitate neuroplasticity. The purpose of this study was to develop a two-axis rehabilitation robot for the distal upper limb, which is controlled by the user-intent through vision assistance in a human-in-the-loop concept (Figure 1). The basic concept was to mount a camera on the exoskeleton, and when the user targets an object to grasp and confirm with a hardware control panel, the user would move the robot to the target using proximal upper limb residual power (human contribution). The orientation of the robot would be adjusted automatically according to the shape and usage of the target image acquired by the camera (robot contribution), and the robot would eventually grasp the object following user confirmation. We also aimed to investigate its clinical feasibility through a pilot usability test. In the next sections, the design and assembly of the new concept rehabilitation robot is presented, followed by a preliminary usability test performed on developers, clinicians, and patients.