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Page 126 Tong et al. Intell Robot 2024;4:125-45 I http://dx.doi.org/10.20517/ir.2024.08

with movement disorders are prone to complications such as muscle wasting, vascular stenosis, and decreased
cardiopulmonary function without theproper rehabilitation training fora long period. To avoid complications
and benefit motor function recovery, such patients need to undergo exercise rehabilitation after acute phase
management such as clinical surgery and medication to restore limb motor function and improve self-care
[2]
ability . Relevant medical literature shows that with early detection and scientific treatment of patients with
movement disorders and limb rehabilitation training, the human nervous system can repair and reconstruct
[3]
the damaged nerves , and there would be a high probability of recovery for patients with mild symptoms,
and basic daily life functions can be realised for patients with more severe symptoms. Therefore, rehabilitation
training plays a crucial role in the recovery of patients with movement disorders. Traditional clinical rehabili-
tation training usually requires professional therapists to provide individual or group rehabilitation guidance
[4]
to patients . However, in China, rehabilitation remains in the initial development stage, facing challenges
such as the majority of patients needing rehabilitation, a long training cycle for physicians, and a shortage of
professional rehabilitation practitioners.

[5]
Therefore, rehabilitation robots have become a research hotspot in the field of robotics . They can not only
reduce the physical burden of therapists but also save medical resources and reduce the cost of rehabilitation
training. Their advantages over traditional manual training methods include: (a) They have the advantage
of repeated training over a long period; (b) The robot-assisted rehabilitation can ensure the consistency of
postural accuracy and intensity of each training session; (c) They have the freedom of time for rehabilitation
training without the influence of manual trainers; (d) The convenience of data recording only requires the
rehabilitation physiotherapist to give training advice on data testing, reducing the cost of hiring a long-term
rehabilitation physiotherapist. It can be seen that robotic rehabilitation training has obvious advantages over
traditional manual rehabilitation training. The research on rehabilitation robots is of great academic and prac-
tical significance.

This study focuses on the current development status of upper limb rehabilitation robots, which can be clas-
[4]
sified into two categories based on their mechanical structure: end-effector-based and exoskeleton-based .
[6]
Representative examples of end-effector-based robots include MIT-Mannus , Mirror Image Motion Enabler
[9]
(MIME) [7,8] , GENTLE/s , and others. This structural type cannot independently drive individual joints of
the upper limb. In contrast, exoskeleton-based robots mimic the physiological structure of the human limbs,
with joint layouts corresponding to those of the human body. Consequently, they can simultaneously guide
coordinated movements of various joints [10] . Robots, such as ARMin [11,12] , Harmony [13] , “u-Rob” [14] , and
RUPERT [15] , fall into the category of exoskeleton robots. Rehabilitation training can be broadly divided into
passive and active training stages. In the passive training process, the robot takes an active role in executing
movements, and the patient is in a passive state, allowing the robot to guide the affected limb through cor-
responding training actions to achieve rehabilitation goals [16] . However, this training process is limited to
patients without muscle strength. Continuous passive training methods do not significantly improve the limb
motor function of patients. Utilising robots as assistants to actively involve patients in rehabilitation training
proves to be a more effective rehabilitation approach [17] . Active training emphasises the rehabilitation robot
following the movement intent of a patient through corresponding assistive control, where the patient takes
the lead, thereby more effectively eliciting spontaneous participation in rehabilitation training [18] . Due to the
substantial joint reduction ratio and the lack of the ability for reverse driving in exoskeleton robots, patients
cannot alter the robot motion trajectory using their own strength [19] . Therefore, achieving assistive control of
exoskeleton robots in an active training mode becomes a challenging problem [13] .

Rehabilitation robots prioritize the estimation of continuous control motion intent in active rehabilitation
training. This method can be broadly categorised into three types: the interaction force-based, the electromyo-
graphic (EMG) signal-based, and the desired trajectory-based methods. The interaction force-based motion
intent estimation method combines force/torque sensors with impedance control. It measures interaction