Performance-Based Iterative Learning Control for Task-Oriented Rehabilitation: A Pilot Study in Robot-Assisted Bilateral Training

• Published in: IEEE transactions on cognitive and developmental systems Vol. 15; no. 4; pp. 2031 - 2040
• Main Authors: Miao, Qing, Li, Zhijun, Chu, Kaiya, Liu, Yudong, Peng, Yuxin, Xie, Shengquan, Zhang, Mingming
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Admittance; Algorithms; Bilateral upper limb; Control algorithms; Control theory; Convergence; End effectors; Force sensors; Fuzzy logic; Human subjects; Indicators; Interactive control; Iterative methods; Machine learning; Muscular fatigue; Parameters; Performance evaluation; performance-based; Regression models; Rehabilitation; Robot control; Robot learning; robot-assisted rehabilitation; Robots; subject-specific; Task analysis; Training; training task planning
• ISSN: 2379-8920; 2379-8939
• DOI: 10.1109/TCDS.2021.3072096

Active participation from human subjects can enhance the effectiveness of robot-assisted rehabilitation. Developing interactive control strategies for customized assistance is therefore essential for encouraging human-robot engagement. However, existing human-robot interactive control strategies lack precise evaluation indicators with effective convergence method to steadily and rapidly customize appropriate assistance during task-oriented training. This study proposes a performance-based iterative learning control algorithm for robot-assisted training, which aims at providing subject-specific robotic assistance to encourage active participation. Three performance indicators based on a Fugl-Meyer assessment (FMA) regression model are introduced to associate clinical scales with robot-based measures, and a fuzzy logic is employed for comprehensive performance evaluation. To increase efficient training time, a piecewise learning rate-based iterative law is applied to quickly converge to a subject-specific control parameter session by session. The proposed strategy is preliminarily estimated for a case of bilateral upper limb training with an end-effector-based robotic system. The experimental results with human subjects indicate that the proposed strategy can obtain appropriate parameters after only several iterations and adapt to random perturbations (like muscle fatigue).