Robot-Assisted Eye-Hand Coordination Training System by Estimating Motion Direction Using Smooth-Pursuit Eye Movements

• Published in: 2023 IEEE International Conference on Robotics and Automation (ICRA) pp. 9852 - 9857
• Main Authors: Li, Xiao, Zeng, Hong, Yang, Chenhua, Song, Aiguo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 29.05.2023
• Subjects: Automation; Force; Games; Haptic interfaces; Robot kinematics; Stroke (medical condition); Training
• DOI: 10.1109/ICRA48891.2023.10160956

Robot-assisted eye-hand coordination rehabilitation training system is extremely urgent to study since recent evidence suggests that eye-hand coordination can be brutally disturbed by stroke with critical consequences on motor behavior. In this paper, we develop a robot-assisted eye-hand coordination training system by estimating motion direction using smooth-pursuit eye movements. Firstly, we design a Pong Game, which requires users to extrapolate the direction of a linearly moving ball and to predict whether this ball would be hit. Secondly, the motion direction of the ball is estimated via smooth-pursuit eye movements, allowing the robot quickly establish an assistive force field to hit the ball. Thirdly, adding haptic feedback technology into this training system to make users more immersive. Finally, we conduct a feasibility study with eight healthy subjects to verify the effectiveness of the proposed system. The experimental results show that the mean success rate for hitting the pong ball of the experiment group (assistance turn-on) is 28.33% higher than that of the control group (assistance turn-off), and the mean interception time of the experiment group is 0.35s shorter than that of the control group. Therefore, the developed system may be promising for transferring to the robot-assisted eye-hand coordination rehabilitation training for post-stroke patients.