Adaptive high-level control for robot-assisted rehabilitation: A Discrete Event System approach

Abstract

This research introduces a two-degree-of-freedom rehabilitation robotic platform to enhance Constraint-Induced Movement Therapy (CIMT) for post-stroke upper limb rehabilitation. Unlike conventional CIMT, that depends on therapist intervention, the proposed system integrates a control framework balancing assistance and autonomy to improve patient engagement and recovery efficiency. The main contribution is a hybrid control architecture combining a low-level impedance controller with a high-level discrete event system (DES) controller. This dual-layer control enables real-time adaptation to a patient’s motor impairment stage, offering dynamic and personalized rehabilitation. The high-level controller, structured around the Chedoke-McMaster Assessment (CMA), facilitates intelligent transitions between rehabilitation states, ensuring robotic assistance matches recovery progress. The design emphasizes simplicity, portability, and user-friendliness, employing a lightweight, cable-driven mechanism that produces smooth and natural movements, closely replicating manual therapy. Experiments with healthy subjects simulating impaired conditions demonstrated the system’s ability to adjust assistance levels and movement velocities according to motor function stages. The results confirm the feasibility of an adaptive, patient-centric control framework that enhances motor engagement and supports progressive rehabilitation. Future work will focus on clinical validation with stroke patients, expanding movement directions, and long-term evaluation of therapeutic outcomes in real-world settings. Overall, this study offers a scalable, data-driven approach bridging robotic automation and therapist-guided rehabilitation, opening new possibilities for improving neuroplasticity and motor recovery after stroke.