Learning Skill Training Schedules from Domain Experts for a Multi-Patient Multi-Robot Rehabilitation Gym

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 31; p. 1
• Main Authors: Adhikari, Bikranta, Bharadwaj, Varun R., Miller, Benjamin A., Novak, Vesna D., Jiang, Chao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Behavioral sciences; Computer simulation; Dynamic scheduling; group rehabilitation; Job shop scheduling; learning from demonstration; Machine learning; Motor skill learning; Multiple robots; Neural networks; Patients; Rehabilitation; rehabilitation gym; Rehabilitation robotics; Rehabilitation robots; Robot sensing systems; Robots; Scheduling; Supervised learning; Synthetic data; Task analysis; task scheduling; Training
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2023.3326777

A robotic gym with multiple rehabilitation robots allows multiple patients to exercise simultaneously under the supervision of a single therapist. The multi-patient training outcome can potentially be improved by dynamically assigning patients to robots based on monitored patient data. In this paper, we present an approach to learn dynamic patient-robot assignment from a domain expert via supervised learning. The dynamic assignment algorithm uses a neural network model to predict assignment priorities between patients. This neural network was trained using a synthetic dataset created in a simulated rehabilitation gym to imitate a domain expert's assignment behavior. The approach is evaluated in three simulated scenarios with different complexities and different expert behaviors meant to achieve different training objectives. Evaluation results show that our assignment algorithm imitates the expert's behavior with mean accuracies ranging from 75.4% to 84.5% across scenarios and significantly outperforms three baseline assignment methods with respect to mean skill gain. Our approach solves simplified patient training scheduling problems without complete knowledge about the patient skill acquisition dynamics and leverages human knowledge to learn automated assignment policies.