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Development of a Nursing Skill Training System Based on Manipulator Variable Admittance Control

Yuhao Zhou, Ryota Takamido, Masako Kanai‐Pak, Jukai Maeda, Yasuko Kitajima, Mitsuhiro Nakamura, Noriaki Kuwahara, Taiki Ogata, Jun Ota

Year
2023
Citations
2

Abstract

The use of robot-based skill training systems is an emerging topic in nursing education, as many innovative robotic systems have been developed to simulate real patients, offering a safe and self-directed platform for nursing students to learn and practice their skills. Among these training systems, several human patient simulators (HPS) have been proposed to simulate the patients performance during patient transfer; however, without an entire motion model and control strategy, most HPS show limited effectiveness in simulating actual patient behavior. Herein, this work presents a novel patient transfer training system that has the potential of improving the practical skills of nursing students. First, we propose a simplified force model for patient transfer motion to estimate the contact force in the absence of wearable sensors. We then reveal the correlation between the nurses force and patients motion during the transfer through the utilization of the variable admittance model. Finally, we demonstrate the feasibility of the proposed patient transfer training system by performing several experiments on a UR10e robot. To the best of our knowledge, this system is the first patient transfer skills training system that simulates force interaction between nurse and patient using a collaborative robot.

Keywords

RobotAdmittanceComputer scienceWearable computerSimulationControl systemControl (management)Human–computer interactionMotion (physics)Motion control

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