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Page 142 Tong et al. Intell Robot 2024;4:125-45 I http://dx.doi.org/10.20517/ir.2024.08

Joint1 Joint2
20
12
Sensor Sensor
10
Identify Identify
15
or Tque Error or Tque Error
8
6
10
4
que[Nm] 5 que[Nm] 2
motor tor 0 motor tor −2 0
−4
0 20 40 60 80 100 120 0 20 40 60 80 100 120
Time(s) Time(s)
Joint3 Joint4
20
6
Sensor Sensor
Identif Identif
or
15 Tque Error or Tque Error
4
10
2
que[Nm] 5 que[Nm]
motor tor motor tor 0
0
−2
0 20 40 60 80 100 120 0 20 40 60 80 100 120
Time(s) Time(s)
Figure 18. Virtual reality training moment data.
illustratedthattheuseofVRtechnologyandthedesignofgamifiedscenesforactiverehabilitationtraininghave
significant effects. Firstly, through visual feedback, intuitively completing the process of placing the wooden
box can motivate patients to take the initiative to complete and enhance their motivation for rehabilitation;
secondly, by setting the initial position of the wooden box in the gamified scene, the range of activities of
the patient’s motor rehabilitation can be adjusted according to the individual’s rehabilitation situation. The
method enhances the relevance and precision of rehabilitation training. Finally, during the execution of one
rehabilitation training cycle, the movement moment curve of the patient is presented in Figure 18, and the
average absolute error is shown in Table 7, with joints 1 and 4 less than 0.8 Nm, joint 2 less than 1 Nm, and
joint 3 less than 1.2 Nm, which are in line with the rehabilitation training needs.

6. CONCLUSIONS
This paper proposes an active rehabilitation training method based on a 5-degree-of-freedom exoskeleton re-
habilitation robot. Meanwhile, a gamified rehabilitation training program with a VR component is designed
for upper limb rehabilitation. The robot modelling, including the MDH parameter table, kinematic rotation
and position matrices calculation, is established first to achieve the passive training. The active rehabilitation
method is built upon the traditional zero-force control algorithm by installing torque sensors at the robot
joints, which can capture the interaction forces between the patient and the robot. An outer-loop PID control
is designed to obtain the feedforward torque compensation values. This method can not only provide com-
pensation torque for zero-force control but also address the issue of inaccurate dynamic models, so as to en-
hance system robustness. Furthermore, dynamic parameters are obtained through the dynamic identification
method that uses a Fourier series excitation trajectory. The dynamics model obtained from the identification
is used for the feedforward compensation function in passive training and the interactive force-assisted con-
trol in active training. In practical application, the fixed PID parameters may not be suitable for patients at
different stages of rehabilitation; thus, a fuzzy control algorithm is designed. Fuzzy PID control demonstrates
flexibility and robustness in active rehabilitation training, adapting well to nonlinearities and uncertainties,
thereby improving system response speed and flexibility. Experiments show that the fuzzy control method
reduces the RMSE and MAE evaluation indexes by more than 15% on average and improves the correlation
coefficient by 4% compared with the traditional PID algorithm. Moreover, the new method effectively reduces
the error surge phenomenon when torque commutation occurs. Finally, based on the proposed outer-loop