Learning Dynamic Patient-Robot Task Assignment and Scheduling for A Robotic Rehabilitation Gym

• Main Authors: Adhikari, Bikranta, Ranashing, Shivanjali, Miller, Benjamin A., Novak, Vesna D., Jiang, Chao
• Format: Conference Proceeding
• Language: English
• Subjects: Assistive robots; Dynamic scheduling; Exercise Therapy - methods; Fitness Centers; Humans; Learning; Neural networks; Robotics - methods; Robots; Simulation; Task analysis; Training
• ISSN: 1945-7901
• DOI: 10.1109/ICORR55369.2022.9896498

A robotic rehabilitation gym is a setup that allows multiple patients to exercise together using multiple robots. The effectiveness of training in such a group setting could be increased by dynamically assigning patients to specific robots. In this simulation study, we develop an automated system that dynamically makes patient-robot assignments based on measured patient performance to achieve optimal group rehabilitation outcome. To solve the dynamic assignment problem, we propose an approach that uses a neural network classifier to predict the assignment priority between two patients for a specific robot given their task success rate on that robot. The priority classifier is trained using assignment demonstrations provided by a domain expert. In the absence of real human data from a robotic gym, we develop a robotic gym simulator and create a synthetic dataset for training the classifier. The simulation results show that our approach makes effective assignments that yield comparable patient training outcomes to those obtained by the domain expert.