Reachable workspace and Robot-assisted personalized rehabilitation training of upper limb

• Published in: 2022 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) pp. 1 - 6
• Main Authors: Bai, Jing, Wen, Xiulan, Nie, Jieyan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.10.2022
• Subjects: Affective computing; Conferences; Force; Kinematics; nonlinear potential field function; reachable workspace; robot; Shoulder; Training; Wrist
• DOI: 10.1109/ACIIW57231.2022.10085998

The increasing number of patients with upper extremity hemiplegia seriously affects their activities of daily living, and robot-assisted rehabilitation training reduce the burden on therapists. Due to the different conditions of patients, the need for personalized rehabilitation training methods is obvious. This paper proposes a method to meet the needs of personalized rehabilitation training. A 9-DOF upper limb kinematic model is constructed, including 2-DOF at the sternoclavicular joint, 3-DOF at the shoulder, 2-DOF at the elbow and 2-DOF at the wrist. A combination of the geometric method and the Levenberg-Marquardt (LM) algorithm is proposed to analyze the inverse kinematics of the upper limbs, the geometric method is used to calculate the initial value, and the LM is iteratively calculated to optimize the trajectory of the upper limbs. The reachable workspace is mapped to the working space of the robot, and a method of combining the reachable workspace of the upper limbs with the nonlinear potential field function is proposed to adjust the change of the force field. Thus, personalized rehabilitation training based on the motion range of the affected limb is realized to meet the needs of different patients. Finally, the effectiveness of the proposed method is verified by experiments.