# Development and Assist-As-Needed Control of an End-Effector Upper Limb Rehabilitation Robot
## 1 Introduction  
Epidemiological studies show that there are more than 10 million new trauma patients every year, among which most of these patients will be accompanied by upper limb motor dysfunction [1]. According to the motor relearning program theory, a certain amount of exercise combined with an effective rehabilitation strategy could restore the upper limb dysfunction. Rehabilitation robots could satisfy the requirements of high-density repetitive movement and complete task-oriented rehabilitation training tasks for the effective recovery of patients with limb disabilities. Clinical studies have proved that upper limb rehabilitation robots could effectively improve the motor function of the upper limb [2; 3], even activities of daily living (ADL) [4]. Various types of upper limb rehabilitation robots have been developed by a lot of Institutions or Universities all over the world during the past 20 years, which falls mainly into two categories [5]: the end-effector type, such as the MIT-MANUS [6], GENTLE/S [7], REHAROB [8], and the exoskeleton type, such as ARMIN [9], Rupert [10], ALEX [11]. The advantage of those robotic systems is that training duration and intensity can be increased compared to conventional therapy. The exoskeleton type upperlimb rehabilitation robot could provide direct assistance and recovery evaluation at each human joint. However, the complex anatomical structure of human upper limbs would lead to additional interactive forces and torques between the exoskeleton robot and human upper limbs during the rehabilitation training [12]. Compared with the exoskeleton upper limb rehabilitation robot, the end-effector-based upper limb rehabilitation robot has a simpler structure and could adapt to different patients better. Despite the shortcomings of the end-effector-based upper limb rehabilitation robot in specific joint training, researches show that it has good effectiveness and superiority in the rehabilitation training of upper limb motor function and ADL compared with the traditional rehabilitation methods [13]. However, most of the existing end-effector-based upper limb rehabilitation robots can only carry out training tasks in a specific plane, which cannot satisfy the requirements of rehabilitation training in three-dimensional space within the human arm's workspace. Besides the mechanical structure and the type of actuators, the explicated controller in the rehabilitation robots has a great impact on the efficiency of the treatment, where increasing efficiency is a crucial factor in reducing the duration and cost of the therapy. A general review of control methods for upper limb assistive robots can be found in the researches [14; 15; 16]. According to the definition of [PERSON] [17], patients in the early stages of neuromuscular injury recovery can hardly move their limbs. Their limbs are passive and require therapists or robots to repeatedly guide the limbs to follow strict patterns. A stiff position controller is required for this type of passive training method. Those position-based passive control strategies are beneficial to patients with severe upper limb movement function impairment. However, as the patient's limb regains partial voluntary motion, the passive training method becomes less effective because it ignores the patient's participation. Studies have shown that for those patients with partial limb movement control ability, the higher degree of active participation in the rehabilitation training process of the patients, and the less intervention of the controllers, the more effective it could induce neuroplasticity and improve the rehabilitation effect [18]. Therefore, for those patients in the middle and later stages of the rehabilitation stage, who have recovered partial upper limb motor ability, it is crucial for the robot to give assistance when the patient cannot complete the task, instead of restricting the patient to follow the predetermined trajectory completely. There is a need to develop a controller that can maximize the benefit of patient engagement.