Liu Xiaoguang, Li Simin, Liang Tie, Li Jun, Lou Cunguang, Wang Hongrui, Liu Xiuling
College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P. R. China.
Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, Hebei 071002, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2022 Dec 25;39(6):1189-1198. doi: 10.7507/1001-5515.202111009.
Gesture imitation is a common rehabilitation strategy in limb rehabilitation training. In traditional rehabilitation training, patients need to complete training actions under the guidance of rehabilitation physicians. However, due to the limited resources of the hospital, it cannot meet the training and guidance needs of all patients. In this paper, we proposed a following control method based on Kinect and NAO robot for the gesture imitation task in rehabilitation training. The method realized the joint angles mapping from Kinect coordination to NAO robot coordination through inverse kinematics algorithm. Aiming at the deflection angle estimation problem of the elbow joint, a virtual space plane was constructed and realized the accurate estimation of deflection angle. Finally, a comparative experiment for deflection angle of the elbow joint angle was conducted. The experimental results showed that the root mean square error of the angle estimation value of this method in right elbow transverse deflection and vertical deflection directions was 2.734° and 2.159°, respectively. It demonstrates that the method can follow the human movement in real time and stably using the NAO robot to show the rehabilitation training program for patients.
手势模仿是肢体康复训练中一种常见的康复策略。在传统康复训练中，患者需要在康复医生的指导下完成训练动作。然而，由于医院资源有限，无法满足所有患者的训练和指导需求。在本文中，我们针对康复训练中的手势模仿任务，提出了一种基于Kinect和NAO机器人的跟随控制方法。该方法通过逆运动学算法实现了从Kinect坐标系到NAO机器人坐标系的关节角度映射。针对肘关节偏转角估计问题，构建虚拟空间平面并实现了偏转角的准确估计。最后，进行了肘关节角度偏转角的对比实验。实验结果表明，该方法在右肘横向偏转角和纵向偏转角方向上的角度估计值的均方根误差分别为2.734°和2.159°。这表明该方法能够利用NAO机器人实时稳定地跟随人体运动，为患者展示康复训练方案。
