Abstract:

Objective To develop a direct teaching technology (teaching and teaching reproduction) of an end-traction upper limb rehabilitation robot, in allusion to the difference between the upper limb training trajectory and training intensity of different patients.

Methods A direct teaching method based on Moveit was proposed, to realize the stable teaching of the upper limb rehabilitation robot, by adding upper limb gravity compensation and constraint step conversion control. After the six-dimensional force sensor collecting the force/torque information, the coordinate transformation, upper limb gravity compensation and constraint step conversion algorithm were used in Robot Operating System (ROS), so that the upper limb of the patient and the end of the mechanical arm could comply with the direction of the drag force of the rehabilitation therapist track, and recorded the teaching track at the same time. In ROS, Moveit was used to write a teaching and reproduction node, which was used to adjust the training times, training speed and rest time of rehabilitation training, and conduct training. Through Moveit's kinematics solution and trajectory planning for the taught trajectory information, the rehabilitation robot could accurately drive the affected limb for reciprocating training, thereby achieving the effect of personalized training.

Results The instructors dragged the upper limbs of five healthy subjects in the horizontal plane, sagittal plane and coronal plane training modes to complete the same training action, and completed any curve movement in the space training mode. The direct teaching took about four to seven seconds and the obtained trajectory was smooth and the direction conformed to the direction of force. Due to difference in the height of the subjects and the length of each part of the upper limbs, the five subjects had different trajectories and positions when completing the same training exercise. The instructor set appropriate training intensity parameters through the human-computer interaction interface and trained five subjects in each mode. The subjects had regular movements and felt comfortable during training. The trajectory of subject three, which was closest to the average value, was selected to train five subjects. It was found that all subjects except subject three had irregular training movements, unstretched or overstretched, etc.

Conclusion The direct teaching system of the end-traction type upper limb rehabilitation robot can well help the instructor teach the multi-degree-of-freedom upper limb training trajectory suitable for different subjects easily, quickly and stably. Rehabilitation training of different intensities is carried out by reproducing the trajectory, which has the characteristics of simple operation, strong pertinence and accurate training.

Key words: terminal traction, upper limb rehabilitation, fast teaching, human-computer interaction, personalized training